Identification of samples in a transition zone

ABSTRACT

During a video encoding or decoding process, a predicted prediction block is generated for a CU. The CU may have two or more prediction units (PUs). A computing device selects a neighbor region size. After the computing device selects the neighbor region size, samples in a transition zone of the prediction block are identified. Samples associated with a first PU are in the transition zone if neighbor regions that contain the samples also contain samples associated with a second PU. Samples associated with the second PU may be in the transition zone if neighbor regions that contain the samples also contain samples associated with the first PU. The neighbor regions have the selected neighbor region size. A smoothing operation is then performed on the samples in the transition zone.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/311,834, filed Dec. 6, 2011, which claims the benefit of U.S.Provisional Application No. 61/431,408, filed Jan. 10, 2011, U.S.Provisional Application No. 61/450,532, filed Mar. 8, 2011, U.S.Provisional Application No. 61/450,538, filed Mar. 8, 2011, and U.S.Provisional Application No. 61/537,450, filed Sep. 21, 2011, the entirecontent of each being hereby incorporated by reference.

TECHNICAL FIELD

This disclosure relates to video coding.

BACKGROUND

Digital video capabilities may be incorporated into a wide range ofdevices, including digital televisions, digital direct broadcastsystems, wireless broadcast systems, personal digital assistants (PDAs),laptop or desktop computers, digital cameras, digital recording devices,digital media players, video gaming devices, video game consoles,cellular or satellite radio telephones, video teleconferencing devices,and the like. Digital video devices implement video compressiontechniques, such as those described in the standards defined by MPEG-2,MPEG-4, ITU-T H.263 or ITU-T H.264/MPEG-4, Part 10, Advanced VideoCoding (AVC), and extensions of such standards, to transmit and receivedigital video information more efficiently.

Video compression techniques perform spatial prediction and/or temporalprediction to reduce or remove redundancy inherent in video sequences.For block-based video coding, a video frame or slice may be partitionedinto blocks. Each block may be further partitioned. Blocks in anintra-coded (I) frame or slice are encoded using spatial prediction withrespect to neighboring blocks. Blocks in an inter-coded (P or B) frameor slice may use spatial prediction with respect to neighboring blocksin the same frame or slice or temporal prediction with respect to otherreference frames.

SUMMARY

Generally, the techniques described in this disclosure may increase thecoding efficiency for a coding unit (CU) by adaptively selecting aneighbor region size. As described in this disclosure, a CU may havemultiple prediction units (PUs). A computing device may perform a motioncompensation operation to generate a prediction block for the CU. Theprediction block may be a two-dimensional block of samples. Each of thesamples may indicate a pixel value. When the computing device performsthe motion compensation operation, the computing device may use motioninformation of the PUs.

In some circumstances, coding efficiency may be enhanced by performing asmoothing operation on samples in a transition zone of the predictionblock. The transition zone is located at a boundary between samples ofthe prediction block associated with different ones of the PUs. Samplesof the prediction block may be in the transition zone if neighborregions that contain the samples also contain samples of the predictionblock that are associated with a different one of the PUs. Differentcoding efficiencies for data associated with the CU may be achieveddepending on the sizes of the neighbor regions. According to techniquesof this disclosure, the sizes of the neighbor regions may be selected bya computing device in order to enhance coding efficiency for dataassociated with the CU.

In one example, this disclosure describes a method of coding video data.The method comprises performing a motion compensation operation togenerate a prediction block for a CU in a frame of the video data. TheCU has a first PU and a second PU. The method also comprises selecting,by a computing device, a first neighbor region size. In addition, themethod comprises after selecting the first neighbor region size,identifying samples in a first transition zone of the prediction block.The first transition zone includes a sample associated with the first PUwhen a neighbor region contains the sample and also contains a sample ofthe prediction block that is associated with the second PU. The neighborregion has the first neighbor region size. The method also comprisesperforming a first smoothing operation on the samples in the firsttransition zone.

In another example, this disclosure describes a computing device thatcodes video data. The computing device comprises a processor configuredto perform a motion compensation operation to generate a predictionblock for a CU in a frame of video data. The CU has a first PU and asecond PU. In addition, the processor is configured to select a firstneighbor region size. The processor is also configured such that afterselecting the first neighbor region size, the processor identifiessamples in a first transition zone of the prediction block. The firsttransition zone includes a sample associated with the first PU when aneighbor region contains the sample and also contains a sample of theprediction block that is associated with the second PU. The neighborregion has the first neighbor region size. In addition, the processor isconfigured to perform a first smoothing operation on the samples in thefirst transition zone.

In another example, this disclosure describes a computing device thatcodes video data. The computing device comprises means for performing amotion compensation operation to generate a prediction block for a CU ina frame of the video data. The CU has a first PU and a second PU. Thecomputing device also comprises means for selecting a neighbor regionsize. In addition, the computing device comprises means for identifying,after selecting the neighbor region size, samples in a transition zoneof the prediction block. The transition zone includes a sampleassociated with the first PU when a neighbor region contains the sampleand also contains a sample of the prediction block that is associatedwith the second PU. The neighbor region has the neighbor region size. Inaddition, the computing device comprises means for performing asmoothing operation on the samples in the transition zone.

In another example, this disclosure describes a computer program productfor coding video data. The computer program product comprises acomputer-readable storage medium having instructions stored thereon thatcause one or more processors to perform a motion compensation operationto generate a prediction block for a CU in a frame of the video data.The CU has a first prediction unit (PU) and a second PU. Theinstructions also cause the processors to select a neighbor region size.In addition, after selecting the neighbor region size, the instructionscause the processors to identify samples in a transition zone of theprediction block. The transition zone includes a sample associated withthe first PU when a neighbor region contains the sample and alsocontains a sample of the prediction block that is associated with thesecond PU. The neighbor region has the neighbor region size. Theinstructions also cause the processors to perform a smoothing operationon the samples in the transition zone.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that illustrates an example multimedia codingsystem.

FIG. 2 is a conceptual diagram that illustrates an example series offrames in a video.

FIG. 3 is a block diagram that illustrates an example configuration ofan encoding unit.

FIG. 4 is a conceptual diagram that illustrates an example framepartitioned into treeblocks.

FIG. 5 is a conceptual diagram that illustrates a further examplepartitioning of treeblocks.

FIG. 6 is a block diagram that illustrates an example configuration ofan inter-prediction unit.

FIG. 7 is a block diagram that illustrates an example configuration of adecoding unit.

FIG. 8 is a flowchart that illustrates an example inter-frame codingoperation performed by an inter-prediction unit.

FIG. 9 is a conceptual diagram that illustrates example rectangularpartitioning modes.

FIG. 10 is a conceptual diagram that illustrates example geometricpartitioning modes.

FIG. 11 is a conceptual diagram that illustrates a transition zone of aCU.

FIG. 12 is a flowchart that illustrates an example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 13 is a flowchart that illustrates another example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 14 is a flowchart that illustrates another example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 15 is a flowchart that illustrates another example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 16 is a flowchart that illustrates another example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 17 is a flowchart that illustrates another example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 18 is a flowchart that illustrates another example operation toadaptively smooth samples in a transition zone of a prediction block.

FIG. 19 is a flowchart that illustrates an example transition sampleidentification operation.

FIG. 20 is a flowchart that illustrates another example operation of asmoothing unit.

FIG. 21 is a flowchart that illustrates another example transitionsample identification operation.

DETAILED DESCRIPTION

The attached drawings illustrate examples. Elements indicated byreference numbers in the attached drawings correspond to elementsindicated by like reference numbers in the following description. In theattached drawings, ellipses indicate the presence of one or moreelements similar to those separated by the ellipses. Alphabeticalsuffixes on reference numbers for similar elements are not intended toindicate the presence of particular numbers of the elements. In thisdisclosure, elements having names that start with ordinal words (e.g.,“first,” “second,” “third,” and so on) do not necessarily imply that theelements have a particular order. Rather, such ordinal words may merelybe used to refer to different elements of the same or similar kind.

A frame of video data is associated with one or more blocks of samples(i.e., a sample block). A sample may be a value defining a component ofa pixel, such as a luma or a chroma component of the pixel. A sampleblock may refer to a two-dimensional array of such samples. Each of thesample blocks of the frame may specify different components of thepixels in the frame. An encoder may first partition a frame into“slices.” A slice is a term used generally to refer to independentlydecodable portions of the frame.

The encoder may next partition these slices into “treeblocks.” Atreeblock may also be referred to as a largest coding unit (LCU). Theencoder may partition the treeblocks into a hierarchy of progressivelysmaller sample blocks, which when illustrated may be represented as ahierarchical tree structure, hence the name “treeblocks.” The leaf nodesof such a hierarchical tree structure may correspond to coding units(CUs) in that these leaf nodes define a sample block or unit that is tobe coded. In this way, each of the CUs is associated with a differentsample block of the frame. Partitioning treeblocks in this way mayenable the encoder to capture motion of different sizes.

Each of the CUs may have one or more prediction units (PUs). The sampleblock of a CU is partitioned into prediction areas associated with thePUs of the CU. A coder may perform a motion estimation operation withrespect to the prediction areas of the sample block to generate motioninformation for each prediction area. The motion information for each ofthe prediction areas is stored as the PU. An encoder or decoder uses themotion information determined for the PUs to generate a prediction blockfor the CU. In this disclosure, the term “coder” may refer to either anencoder or a decoder and the term “coding” may refer to either encodingor decoding.

In some instances, the coder performs a smoothing operation to smoothsamples in a transition zone of the prediction block. The transitionzone occurs generally at a boundary between samples of prediction blockthat are associated with different PUs. The coder may perform thissmoothing operation to improve subsequent coding of residual data of theCU and/or resulting video quality of the video data when reconstructedby a decoder (where such video data may be referred to as “reconstructedvideo data”).

While in some instances smoothing the samples in the transition zoneimproves the compression performance and/or visual quality of thereconstructed video data, in other instances performing of the smoothingoperation introduces artificial effects that degrade compressionperformance. Thus, performing the smoothing operation may result incompressed video data that is larger in size due to the introduction ofadditional residual data by the smoothing operation. Moreover, theseintroduced artificial effects may degrade the subjective perception ofthe reconstructed video data when viewed by a viewer, which impactssubjective video quality. In some other instances, performing thissmoothing operation may remove data in the video content that may beconsidered important to a particular frame, which may further reducevisual quality and impact reconstruction of other frames that rely onthis frame for reconstruction of the frame. The loss of data throughperformance of this smoothing operation may, furthermore, beirreversible, meaning that the decoder may not be able to recover thelost data.

This disclosure provides techniques for adaptively performing asmoothing operation in a manner that potentially improves visual qualityand compression performance. Rather than perform a smoothing operationto smooth samples in transition zones of each and every predictionblock, as is common in conventional encoders, the techniques enable acoder to adaptively perform the smoothing operation in instances wherethe coder determines that it would be most beneficial, as measured interms of visual quality or compression performance. For instance, thecoder may adaptively perform the smoothing operation based on a size ofone or more PUs of a CU.

With a large transition zone, more samples may be manipulated. However,processing too many samples may introduce extra distortion. With a smalltransition zone, the performance of the smoothing operation may belimited. In accordance with some techniques described in thisdisclosure, the coder adaptively selects a neighbor region size. Thecoder uses regions of the selected neighbor region size to determinewhether samples of the prediction block are within the transition zone.In this way, the coder may select the size of the transition zone tomaximize the performance of the smoothing operation.

FIG. 1 is a block diagram that illustrates an example multimedia codingsystem 100. Multimedia coding system 100 captures video data, encodesthe captured video data, transmits the encoded video data, decodes theencoded video data, and then plays back the decoded video data.

Multimedia coding system 100 comprises a source unit 102, an encodingunit 104, a decoding unit 106, and a presentation unit 108. Source unit102 generates video data. Encoding unit 104 encodes the video data.Decoding unit 106 decodes the encoded video data. Presentation unit 108presents the decoded video data.

One or more computing devices implement source unit 102, encoding unit104, decoding unit 106, and presentation unit 108. In this disclosure,the term computing device encompasses physical devices that processinformation. Example types of computing devices include personalcomputers, laptop computers, mobile telephones, smartphones, tabletcomputers, in-car computers, television set-top boxes, videoconferencing systems, video production equipment, video cameras, videogame consoles, or others types of devices that process information.

In some examples, a single computing device may implement two or more ofsource unit 102, encoding unit 104, decoding unit 106, and presentationunit 108. For example, a single computing device may implement sourceunit 102 and encoding unit 104. In this example, another computingdevice may implement decoding unit 106 and presentation unit 108. Inother examples, different computing devices implement source unit 102,encoding unit 104, decoding unit 106, and presentation unit 108.

In the example of FIG. 1, a computing device 103 implements encodingunit 104 and a computing device 107 implements decoding unit 106. Insome examples, computing device 103 may provide functionality inaddition to encoding unit 104. Furthermore, in some examples, computingdevice 107 may provide functionality in addition to decoding unit 106.

As mentioned briefly above, source unit 102 generates video data thatrepresent a series of frames. A frame is also commonly referred to as a“picture.” When the series of frames in the video data are presented toa user in rapid succession (e.g., 24 or 25 frames per second), the usermay perceive objects in the frames to be in motion.

FIG. 2 is a conceptual diagram that illustrates an example series offrames 200A through 200P in video data. This disclosure referscollectively to frames 200A through 200P as “frames 200.” The video datarepresents scenes of a bicycle race. The frames in rows 202 and 204 showa scene of a person pedaling a bicycle. The frames in row 206 show twocommentators sitting behind a desk. The frames in row 208 show a sceneof bicycle racers from overhead. Each frame within a scene may differslightly from the preceding frame. By presenting frames 200 in rapidsuccession, users may perceive the motion in these scenes.

Continuing reference is now made to the example of FIG. 1. In variousexamples, source unit 102 generates the video data in various ways. Forexample, source unit 102 may comprise a video camera. In this example,the video camera captures images from a visible environment. In anotherexample, source unit 102 may comprise one or more sensors for medical,industrial, or scientific imaging. Such sensors may include x-raydetectors, magnetic resonance imaging sensors, particle detectors, andso on. In yet another example, source unit 102 may comprise an animationsystem. In this example, one or more users may use the animation systemto draw, draft, program, or otherwise design the content of the videodata from their imaginations.

Encoding unit 104 receives the video data generated by source unit 102.Encoding unit 104 encodes the video data such that less data representsthe series of frames in the video data. In some instances, encoding thevideo data in this way may be necessary to ensure that the video datamay be stored on a given type of computer-readable media, such as a DVDor CD-ROM. Furthermore, in some instances, encoding the video data inthis way may be necessary to ensure that the video data may beefficiently transmitted over a communication network, such as theInternet.

Encoding unit 104 may encode video data, which is often expressed as asequence or series of video frames. To encode the video data, encodingunit 104 may split these frames into independently decodable portions(which are commonly referred to as “slices”), which in turn, encodingunit 104 may split into treeblocks. These treeblocks may undergo a formof recursive hierarchical quadtree splitting. Encoding unit 104 mayperform this splitting to generate a hierarchical tree-like datastructure, with the root node being referred to as a “treeblock.” Theleaf nodes of this hierarchical tree-like data structure may be referredto as “coding nodes.” A “coding unit” or “CU” includes the coding nodeas well as other types of information, including motion information andtransform information. Each coding node identifies a sample block withina treeblock. In this disclosure, the sample block identified by a codingnode associated with a CU may be referred to as the sample block of theCU.

Encoding unit 104 may use rectangular and/or geometric partitioningmodes to partition the sample block of a CU into prediction areas. Whenencoding unit 104 uses a geometric partitioning mode to partition thesample block of a CU, a boundary between the partition areas might notmeet the edges of the sample block at right angles. Encoding unit 104may perform a form of motion estimation with respect to each predictionarea to generate motion information, such as a motion vector, for eachprediction area. A “prediction unit” or “PU” of a CU may containinformation indicating a prediction area of the CU, the motioninformation for the prediction area, and/or other information regardingthe prediction area. Encoding unit 104 may use the motion information togenerate a prediction block.

In accordance with the techniques of this disclosure, encoding unit 104may make a determination based on a size of a first PU of the CU whetherto perform a smoothing operation. The transition zone occurs at aboundary between samples of the prediction block associated with thefirst PU of the CU and samples of the prediction block associated with asecond PU of the CU. After encoding unit 104 makes the determination toperform the smoothing operation, encoding unit 104 may perform thesmoothing operation to smooth the samples in the transition zone of theprediction block.

Furthermore, in accordance with the techniques of this disclosure,encoding unit 104 may determine whether samples of the prediction blockare in the transition zone by selecting a neighbor region size. Afterselecting the neighbor region size, encoding unit 104 may identifysamples in the transition zone of the prediction block. The transitionzone includes a sample associated with a first PU of the CU when aneighbor region contains the sample and also contains a sample of theprediction block that is associated with a second PU of the CU. Theneighbor region has the selected neighbor region size.

Encoding unit 104 then determines residual data by comparing theoriginal sample block of the CU to the prediction block. Afterdetermining this residual data, encoding unit 104 may apply a transformto residual data. Thus, encoding unit 104 applies the smoothingoperation to the samples in the transition zone of the prediction blockbefore applying the transform to the residual data derived from theprediction block. To transform this residual data, encoding unit 104 maypartition the residual data into one or more transform areas. Encodingunit 104 then applies one or more transforms to the transform areas ofthe residual data to generate transformed residual data, which may alsobe referred to as a “transform coefficient block.” This transformcoefficient block typically expresses the residual data as a block oftransform coefficients. The transformed residual data is then stored toa transform unit (TU) of the corresponding CU. Thus, a CU comprises acoding node or sample block, a TU and a CU, as well as, any other syntaxelements that may be necessary to decode the transformed residual data.

Decoding unit 106 receives encoded video data. In various examples,decoding unit 106 may receive the encoded video data in various ways.For example, decoding unit 106 may receive a computer-readable medium,such as a DVD, that stores the video data. In another example, decodingunit 106 may receive the encoded video data from a communication medium,such as the Internet, a local area network (LAN), a cable connected toanother computing device, or a wireless networking link.

After receiving the encoded video data, decoding unit 106 decodes theencoded video data. When decoding unit 106 decodes the encoded videodata, decoding unit 106 may generate a prediction block for a CU of aframe in the video data. Decoding unit 106 may then determine whether toperform a smoothing operation on samples in the transition zone of theprediction block. The transition zone of the predicted block may be at aboundary between samples of the prediction block associated with a firstPU of the CU and samples of the prediction block associated with asecond PU of the CU. After decoding unit 106 makes the determination toperform the smoothing operation on the samples in the transition zone,decoding unit 106 performs the smoothing operation to smooth the samplesin the transition zone of the prediction block.

Furthermore, in accordance with the techniques of this disclosure,decoding unit 106 may determine whether samples of the prediction blockare in the transition zone by selecting a neighbor region size. Afterselecting the neighbor region size, decoding unit 106 may identifysamples in the transition zone of the prediction block. The transitionzone includes a sample associated with a first PU of the CU when aneighbor region contains the sample and also contains a sample of theprediction block that is associated with a second PU of the CU. Theneighbor region has the selected neighbor region size.

Presentation unit 108 receives the decoded video data from decoding unit106. In various examples, presentation unit 108 receives the decodedvideo data in various ways. For example, where a single computing deviceprovides decoding unit 106 and the presentation system 108, presentationunit 108 may receive the decoded video data via one or more internalcommunication media, such as cables or buses. In another example,presentation unit 108 may receive the decoded video data from one ormore computer-readable media, such as a network connection, DVD, CD-ROM,solid-state memory device, and so on. After receiving the decoded videodata, presentation unit 108 presents the frames in the decoded videodata to one or more users.

FIG. 3 is a block diagram that illustrates an example configuration ofencoding unit 104. In the example of FIG. 3, encoding unit 104 providesa mode select unit 302, an inter-prediction unit 304, anintra-prediction unit 308, a residual generation unit 310, a transformmodule 312, a quantization unit 314, an entropy coding unit 316, aninverse quantization unit 318, an inverse transform unit 320, areconstruction unit 322, and a reference frame store 324. Readers willunderstand that some examples of encoding unit 104 may comprise more,fewer, or different units.

In various examples, encoding unit 104 implements mode select unit 302,inter-prediction unit 304, intra-prediction unit 308, residualgeneration unit 310, transform module 312, quantization unit 314,entropy coding unit 316, inverse quantization unit 318, inversetransform unit 320, reconstruction unit 322, and reference frame store324 in various ways. For example, the one or more computing devices thatimplement encoding unit 104 may implement one or more of these unitswhen processors of the one or more computing devices execute certaincomputer-readable instructions stored on one or more computer-readablemedia. In this example, these units or modules may or may not beimplemented as discrete, modular pieces of computer software. In anotherexample, the one or more computing devices that implement encoding unit104 may comprise one or more application-specific integrated circuits(ASICs) that implement the functionality of one or more of these units.In some examples, the functionality of these units may be provided byseparate computing devices.

Encoding unit 104 receives data representing frames of video data. Whenencoding unit 104 receives data representing a frame, encoding unit 104encodes the frame. For ease of explanation, this disclosure refers tothe frame being encoded as the source frame. The data representing thesource frame comprises one or more blocks of samples.

To encode the source frame, mode select unit 302 partitions a sampleblock of the frame among a plurality of treeblocks. In some instances, atreeblock may be an N×N block of luma samples and two correspondingblocks of chroma samples. In some examples, a block is two-dimensionalarray of samples or transform coefficients. In other instances, atreeblock may be block of luma samples or a chroma sample array.

Mode select unit 302 may generate a quadtree for each of the treeblocks.The quadtree for a treeblock comprises a hierarchy of nodes. Initially,the quadtree of the given treeblock only comprises a root node. The rootnode corresponds to the given treeblock. Mode select unit 302 maypartition the given treeblock into multiple smaller sample blocks. Whenmode select unit 302 partitions the given treeblock into multiplesmaller sample blocks, mode select unit 302 adds child nodes to thequadtree of the given treeblock. Each of the child nodes corresponds toa different one of the smaller sample blocks. In some examples, modeselect unit 302 may subdivide one or more of the smaller sample blocksinto yet smaller sample blocks. When mode select unit 302 partitions asmaller sample blocks into yet smaller sample blocks, mode select unit302 may add grandchild nodes to the quadtree of the given treeblock.Each of the grandchild nodes corresponds to one of the yet smallersample blocks. The grandchild nodes are children of the child nodes.Mode select unit 302 may continue partitioning the given treeblock andgenerating nodes in the quadtree of the given treeblock as appropriate,up to a pre-configured limit. Nodes in the quadtree that have no childnodes (i.e., leaf nodes) are referred herein as coding nodes.

Each of the coding nodes corresponds to a different CU. A coding node ofa CU is a root node of a prediction tree and a transform tree. Theprediction tree stores information of PUs of the CU. For example, theprediction tree may specify sizes and positions of prediction areas ofthe PUs. The PUs of the CU may also comprise additional associatedprediction data. The transform tree stores information regarding TUs ofthe CU. For example, the transform tree may specify sizes and positionsof transform area of the TUs. The TUs of the CU may also compriseadditional associated transform data.

FIG. 4 is a conceptual diagram that illustrates example frame 200Apartitioned into treeblocks 400A through 400P (collectively, “treeblocks400”). Each of treeblocks 400 is square and has the same size. Forexample, the sample blocks of treeblocks 400 may be 32 samples wide by32 samples high (i.e., 32×32). In another example, the sample blocks oftreeblocks 400 may be 64 samples wide by 64 samples high (i.e., 64×64).

FIG. 5 is a conceptual diagram that illustrates a further examplepartitioning of treeblocks 400. In the example of FIG. 5, mode selectunit 302 has partitioned the sample block of treeblock 400J into foursmaller sample blocks 500A through 500D. Furthermore, in the example ofFIG. 5, mode select unit 302 has partitioned sample block 400D into foursample blocks 502A through 504D. Mode select unit 302 has furthersubdivided sample block 502A into four more sample blocks 504A through504D.

Continuing reference is now made to the example of FIG. 3. After modeselect unit 302 generates a quadtree for a treeblock, inter-predictionunit 304 performs an inter-frame coding operation for each CU of thetreeblock. When inter-prediction unit 304 performs the inter-framecoding operation for a CU, inter-prediction unit 304 uses rectangularand/or geometric partitioning modes to partition the sample block of theCU into prediction areas. The PUs of the CU specify these predictionareas.

After inter-prediction unit 304 uses a given partitioning mode topartition the sample block into two or more prediction areas,inter-prediction unit 304 may perform a motion estimation operation thatgenerates motion information for the PUs associated with theseprediction areas. During the motion estimation operation,inter-prediction unit 304 searches a reference frame for referencesamples for the PUs. A reference sample for a PU is a portion of areference frame that corresponds to the samples in the prediction areaof the PU. Inter-prediction unit 304 generates motion information for aPU to indicate the reference sample for the PU.

Inter-prediction unit 304 uses the reference samples for the PUs of a CUto generate a prediction block for the CU. The prediction block of a CUis a block of predicted samples. The prediction block for a CU maydiffer somewhat from the sample block of the CU. For example, samples inthe prediction block may have slightly different colors or brightnessesfrom the corresponding samples of the sample block of the CU.

In accordance with the techniques of this disclosure, inter-predictionunit 304 may make a determination whether to perform a smoothingoperation on samples in a transition zone of the prediction block. Ifinter-prediction unit 304 makes the determination to perform thesmoothing operation on the samples in the transition zone,inter-prediction unit 304 perform the smoothing operation to smooth thesamples in the transition zone. Furthermore, inter-prediction unit 304may adaptively identify samples in the transition zone of the predictionblock. For example, inter-prediction unit 304 may select a neighborregion size. After selecting the neighbor region size, inter-predictionunit 304 may identify samples in the transition zone. In this example,the transition zone includes a sample associated with a first PU of theCU when a neighbor region contains the sample and also contains a sampleof the prediction block that is associated with a second PU of the CU.The neighbor region has the selected neighbor region size.

Intra-prediction unit 308 may use samples in the sample blocks of otherCUs of the source frame to generate a prediction block for the CU. Invarious examples, intra-prediction unit 308 generates the predictionblock in various ways. For example, intra-prediction unit 308 maygenerate the prediction block of the CU such that samples in neighboringCUs extend horizontally across or vertically down through the predictionblock. Intra-prediction unit 308 may also select an intra-predictionmode that best corresponds to the sample block of the CU.

After inter-prediction unit 306 and intra-prediction unit 308 generateprediction blocks for the CU, mode select unit 302 may select one of theprediction blocks for the CU. If the mode select unit 302 selects aprediction block generated by intra-prediction unit 308, mode selectunit 302 may add a syntax element to the coding node of the CU toindicate the intra-prediction mode that intra-prediction unit 308 usedwhen generating the selected prediction block. If mode select unit 302selects a prediction block generated by inter-prediction unit 304, modeselect unit 302 may add a syntax element to the coding node for the CUthat indicates that inter-prediction was used to encode the CU. Inaddition, mode select unit 302 may add syntax elements to the predictiontree of the CU. For example, mode select unit 302 may add syntaxelements to the prediction tree indicating the sizes and locations ofPUs of the CU, motion vectors for the PUs, and other data generatedduring the inter-frame coding operation. Furthermore, mode select unit302 may add syntax elements to the transform tree of the CU. Forexample, mode select unit 302 may add syntax elements to the transformtree indicating the sizes and locations of TUs of the CU.

In some examples, a syntax element is an element of data represented ina bitstream. A bitstream may be a sequence of bits that forms arepresentation of coded pictures and associated data forming one or morecoded video sequences. A coded video sequence may be a sequence ofaccess units. An access unit may be a set of Network Abstraction Layer(NAL) units that are consecutive in decoding order and contain exactlyone primary coded picture. A NAL unit may be a syntax structurecontaining an indication of the type of data to follow and bytescontaining that data in the form of a raw byte sequence payloadinterspersed as necessary with emulation prevention bits. A primarycoded picture may be a coded representation of a picture to be used by adecoding process for a bitstream.

After mode select unit 302 selects a prediction block of the CU,residual generation unit 310 may use the original sample block of the CUand the selected prediction block of the CU to generate residual datafor the CU. In some examples, the residual data for the CU may bearranged as a two-dimensional array of the residual data (i.e., aresidual block). The residual data for the CU may represent thedifferences between the original sample block of the CU and theprediction block of the CU. In various examples, residual generationunit 310 may generate the residual data in various ways. For example,residual generation unit 310 may generate the residual data for the CUby subtracting the samples in the prediction block of the CU from thesamples in the sample block of the CU.

As mentioned briefly above, each CU has one or more TUs. A transformunit may comprise a transform tree and associated transform data. Thetransform tree may specify the sizes and positions of transform areas.For example, the transform tree may indicate the location of an upperleft corner of a transform area. In this example, the size of thetransform area may be derived from a depth of a corresponding node inthe transform tree.

When residual generation unit 310 generates residual data for the CU,transform module 312 may perform a transform operation for each TU ofthe CU. When transform module 312 performs the transform operation for aTU of the CU, transform module 312 transforms applicable samples of theresidual data from a spatial domain to a frequency domain. Transformmodule 312 may store samples in the frequency domain as a transformcoefficient block. The applicable samples of the residual data mayinclude samples of the residual data in the transform area specified bythe TU. The transform coefficient block is a two-dimensional array oftransform coefficients. In some examples, a transform coefficient may bea scalar quantity, considered to be in a frequency domain, that isassociated with a particular one-dimensional or two-dimensionalfrequency index in an inverse transform part of a decoding process.

When transform module 312 performs the transform operation on samples ofthe residual data, transform module 312 applies a mathematicaltransformation to the samples. For example, transform module 312 mayperform a Discrete Cosine Transform (DCT) on the samples to transformthe samples from the spatial domain to the frequency domain.

Transform module 312 may provide the resulting transform coefficientblock to quantization unit 314. Quantization unit 314 may perform aquantization operation on the transform coefficient block. Whenquantization unit 314 performs the quantization operation, quantizationunit 314 may quantize each of the transform coefficients in thetransform coefficient block, thereby generating a quantized transformcoefficient block. The quantized transform coefficient block is atwo-dimensional array of quantized transform coefficients. In variousexamples, quantization unit 314 performs various quantizationoperations. For example, quantization unit 314 may perform aquantization operation that quantizes the transform coefficients bydividing the transform coefficients by a quantization parameter and thenclipping the resulting quotients.

After quantization unit 314 performs the quantization operation on thetransform coefficient blocks of the CU, entropy coding unit 316 performsan entropy coding operation on the quantized transform coefficient blockof the CU, the coding node of the CU, the prediction tree of the CU, andthe transform tree of the CU. Entropy coding unit 316 generates entropycoded data for the CU as a result of performing this entropy codingoperation. In some instances, when entropy coding unit 316 performs theentropy coding operation, quantization unit 314 may reduce the number ofbits needed to represent the data of the CU. In various instances,entropy coding unit 316 may perform various entropy coding operations onthe data of the CU. For example, entropy coding unit 316 may perform acontext-adaptive variable-length coding (CAVLC) operation or acontext-adaptive binary arithmetic coding (CABAC) operation on the dataof the CU.

Encoding unit 104 generates a bitstream that includes entropy encodeddata for the CU. In various examples, encoding unit 104 may generatevarious types of bitstreams that include the entropy encoded data forthe CU. For example, encoding unit 104 may output a NAL unit stream. Inthis example, the NAL unit stream comprises a sequence of syntaxstructures called NAL units. The NAL units are ordered in decodingorder. The one or more of the NAL units may include the entropy encodeddata for the CU. In another example, encoding unit 104 may output a bytestream. Encoding unit 104 constructs the byte stream from a NAL unitstream by ordering NAL units in decoding order and prefixing each NALunit with a start code prefix and zero or more zero-value bytes to forma stream of bytes.

Inverse quantization unit 318 performs an inverse quantization operationon quantized transform coefficient blocks. The inverse quantizationoperation at least partially reverses the effect of the quantizationoperation performed by quantization unit 314, thereby generatingtransform coefficient blocks.

Inverse transform unit 320 performs an inverse transform operation ontransform coefficient blocks generated by inverse quantization unit 318.When inverse transform unit 320 performs the inverse transformoperation, inverse transform unit 320 reverses the effect of thetransformation operation performed by transform module 312, therebygenerating reconstructed residual data.

Reconstruction unit 322 performs a reconstruction operation thatgenerates reconstructed sample blocks. Reconstruction unit 322 generatesthe reconstructed sample blocks based on the reconstructed residual dataand the prediction blocks generated by inter-prediction unit 304 orintra-prediction unit 308. In various examples, reconstruction unit 322performs various reconstruction operations. For example, reconstructionunit 322 performs the reconstruction operation by adding the samples inthe reconstructed residual data with corresponding samples in theprediction blocks.

Reference frame store 324 stores the reconstructed sample blocks. Afterencoding unit 104 has encoded data for each CU of the source frame,encoding unit 104 has generated reconstructed sample blocks for each CUin the source frame. Hence, reference frame store 324 stores a completereconstruction of the sample block of the source frame. Mode select unit302 may provide the reconstructions of the sample blocks of the sourceframe to inter-prediction unit 304 as a reference frame.

FIG. 6 is a block diagram that illustrates an example configuration ofinter-prediction unit 304. In the example of FIG. 6, inter-predictionunit 304 comprises a motion estimation unit 602, a motion compensationunit 604, and a TU generation unit 606. Motion compensation unit 604comprises a smoothing unit 608. Readers will understand that otherexample configurations of inter-prediction unit 304 may include more,fewer, or different components.

Motion estimation unit 602 may perform a motion estimation operation foreach PU of a CU. Motion compensation unit 604 may perform a motioncompensation operation that generates a prediction block for the CU. TUgeneration unit 606 may perform a transform unit selection operationthat generates TUs of the CU. In accordance with the techniques of thisdisclosure, smoothing unit 608 may adaptively perform a smoothingoperation on samples in a transition zone of the prediction block. Inaddition, smoothing unit 608 may determine which samples of theprediction block are in the transition zone. Although FIGS. 12-21 aredescribed as operations performed by smoothing unit 608, the operationsdescribed in FIGS. 12-21 may be performed by a motion compensation unitof decoding unit 106.

When motion estimation unit 602 performs the motion estimation operationfor the CU, motion estimation unit 602 may generate one or moreprediction trees. Each of the prediction trees may be associated with adifferent PU of the CU. Each of the prediction trees may specify aposition and a size of a prediction area. For ease of explanation, thisdisclosure may refer to the position or size of the prediction areaspecified by the prediction tree of a PU as the position or size of thePU.

Motion estimation unit 602 may search one or more reference frames forreference samples. The reference samples of a PU may be areas of thereference frames that visually correspond to portions of the sampleblock of the CU that fall within the prediction area of the PU. Ifmotion estimation unit 602 finds such a reference sample for one of thePUs, motion estimation unit 602 may generate a motion vector. The motionvector is a set of data that describes a difference between the spatialposition of the reference sample for a PU and the spatial position ofthe PU. For example, the motion vector may indicate that the referencesample of a PU is five samples higher and three samples to the right ofthe PU. Furthermore, in some circumstances, motion estimation unit 602may not be able to identify a reference sample for a PU. In suchcircumstances, motion estimation unit 602 may select a skip mode or adirect mode for the PU.

In some examples, rather than performing a search for a reference sampleof a PU, motion estimation unit 602 may predict a motion vector for thePU. In performing this motion vector prediction, motion estimation unit602 may select one of the motion vectors determined for spatiallyneighboring CUs in the source frame or a motion vector determined for aco-located CU in a reference frame. Motion estimation unit 602 mayperform motion vector prediction rather than search for a referencesample in order to reduce complexity associated with determining amotion vector for each partition.

Motion compensation unit 604 uses the inter-coding modes of the PUs togenerate the prediction block of the CU. If motion estimation unit 602selected the skip mode for a PU, motion compensation unit 604 maygenerate the prediction block of the CU such that samples in theprediction block that are associated with the PU match collocatedsamples in the reference frame. If motion estimation unit 602 selectedthe direct mode for a PU, motion compensation unit 604 may generate theprediction block such that samples in the prediction block that areassociated with the PU match collocated samples in the sample block ofthe CU. If motion estimation unit 602 generated a motion vector for aPU, motion compensation unit 604 may generate the prediction block suchthat samples in the prediction block that are associated with the PUcorrespond to samples in a portion of the reference frame indicated bythe motion vector. If motion estimation unit 602 generated multiplemotion vectors for a PU, motion compensation unit 604 may generate theprediction block such that samples in the prediction block that areassociated with the PU correspond to samples in portions of multiplereference frames indicated by the motion vectors of the PU.

FIG. 7 is a block diagram that illustrates an example configuration ofdecoding unit 106. As illustrated in the example of FIG. 7, decodingunit 106 implements an entropy decoding unit 700, a motion compensationunit 702, an intra-prediction unit 704, an inverse quantization unit708, an inverse transform module 710, a reconstruction unit 712, and areference frame store 714. In various examples, decoding unit 106implements these components in various ways. For example, the one ormore computing devices that provide decoding unit 106 may implementthese units when processors of the computing devices execute certaincomputer-readable instructions. In this example, these units or modulesmay or may not be implemented as discrete, modular pieces of computersoftware. In another example, the one or more computing devices thatimplement decoding unit 106 may comprise ASICs that provide thefunctionality of one or more of these units.

Decoding unit 106 receives an encoded bitstream that represents videodata. The encoded bitstream may comprise data representing frames in thevideo data. For example, the encoded bitstream may comprise datarepresenting each of frames 200 (FIG. 2). When decoding unit 106receives data representing for a frame, decoding unit 106 decodes thedata to reconstruct the frame. For ease of explanation, this disclosuremay refer to this frame as the source frame.

When decoding unit 106 decodes the data for the source frame, decodingunit 106 receives encoded data for each CU of the source frame. Forexample, decoding unit 106 may receive an encoded version of a quantizedtransform coefficient block for the CU, an encoded version of the codingnode of the CU, an encoded version of the prediction tree of the CU, andan encoded version of the transform tree of the CU. Decoding unit 106then decodes the data of each CU of the source frame. When decoding unit106 decodes the data of a given CU, entropy decoding unit 700 receivesencoded data for the given CU. Entropy decoding unit 700 performs anentropy decoding operation on the encoded data for the given CU. Theentropy decoding operation reverses the effects of the entropy codingoperation performed by entropy coding unit 316 (FIG. 3).

Entropy decoding unit 700 provides the quantized transform coefficientblock for the CU to inverse quantization unit 708. Entropy decoding unit700 may provide coding data for the CU, such as the coding node,prediction tree, and transform tree of the CU, to motion compensationunit 702 and/or intra-prediction unit 704.

When motion compensation unit 702 receives the coding data for the CU,motion compensation unit 702 uses the coding data to perform a motioncompensation operation that generates a prediction block for the CU.During the motion compensation operation, motion compensation unit 702may retrieve one or more reference frames from reference frame store714. The motion compensation unit 702 may then identify referencesamples for PUs of the CU. The motion vectors for the PUs identify areaswithin the references frames as the reference samples for the PUs. Afteridentifying the reference samples for PUs of the CU, motion compensationunit 702 generates a prediction block for the CU. In the predictionblock, PUs of the CU may contain the reference samples of the PUs.

In accordance with the techniques of this disclosure, motioncompensation unit 702 may perform an operation that adaptively performsa smoothing operation on samples in a transition zone of the predictionblock. For example, motion compensation unit 702 may perform one of theexample operations illustrated in FIGS. 12-18 and 20. Furthermore, insome instances, motion compensation unit 702 may determine which samplesare within the transition zone. In some examples, motion compensationunit 702 may determine which samples are within the transition zone byperforming the example operations illustrated in FIGS. 20 and 21.

When intra-prediction unit 704 receives the coding data for the CU,intra-prediction unit 704 uses the reconstructed sample blocks ofpreviously decoded sample blocks in the source frame to generate theprediction block for the CU. Intra-prediction unit 704 may modify thesamples in the prediction block according to an indicatedintra-prediction mode.

Inverse quantization unit 708 receives one or more quantized transformcoefficient blocks for each CU. When inverse quantization unit 708receives quantized transform coefficient block for a CU, inversequantization unit 708 performs an inverse quantization operation that atleast partially reverses the effect of the quantization operationperformed by quantization unit 314 (FIG. 3), thereby generating anon-quantized transform coefficient block for the CU.

Inverse transform module 710 performs an inverse transform operation ontransform coefficient blocks. The inverse transform operation mayreverse the effect of the transformation operation performed bytransform module 312 (FIG. 3), thereby generating reconstructed residualdata. Inverse transform module 710 provides the reconstructed residualdata to reconstruction unit 712.

Reconstruction unit 712 receives prediction blocks from motioncompensation unit 702 and intra-prediction unit 704. Reconstruction unit712 also receives corresponding reconstructed residual data from inversetransform module 710. Reconstruction unit 712 performs a reconstructionoperation that uses reconstructed residual data of a CU and a predictionblock for the CU to generate a reconstructed sample block for the CU. Invarious examples, reconstruction unit 712 may perform variousreconstruction operations. For example, reconstruction unit 712 maygenerate the reconstructed sample block of the CU by adding the samplesin the reconstructed residual data of the CU with corresponding samplesin the prediction block of the CU.

After generating a reconstructed sample block for a CU, reconstructionunit 712 outputs the reconstructed sample block. Reconstruction unit 712also provides the reconstructed sample block to reference frame store714. Reference frame store 714 stores the reconstructed sample block.Motion compensation unit 702 and/or intra-prediction unit 704 maysubsequently use the reconstructed sample block to generate additionalprediction blocks.

FIG. 8 is a flowchart that illustrates an example inter-frame codingoperation 800 performed by inter-prediction unit 304. After encodingunit 104 starts inter-frame coding operation 800, motion estimation unit602 performs a motion estimation operation on PUs of the CU (802). Insome instances, motion estimation unit 602 may perform the motionestimation operation for a PU by searching within a search area of areference frame for a reference sample for the PU. The reference samplefor the PU may be a portion of the reference frame that corresponds toprediction area specified by the prediction tree of the PU. If motionestimation unit 602 finds a reference sample for the PU, motionestimation unit 602 may generate a motion vector that indicates adifference in position between the PU and the reference sample for thePU. In other instances, motion estimation unit 602 may predict a motionvector for the PU.

Within this general framework, various examples of motion estimationunit 602 may perform the motion estimation operation in various ways.For example, different examples of motion estimation unit 602 may usedifferent search areas when searching for a portion of the referenceframe that corresponds to a PU. In some such examples, encoding unit 104has a search area parameter that controls the size of the search areaused by motion estimation unit 602. The value of the search areaparameter may be set by a human user or by a computer program.

After performing the motion estimation operation on the PUs of the CU,motion compensation unit 604 performs a motion compensation operation togenerate a prediction block for the CU (804). Furthermore, duringperformance of operation 800, smoothing unit 608 may perform anoperation that adaptively performs a smoothing operation on samples in atransition zone of the prediction block (806). The smoothing operationmay smooth the samples in a transition zone of the prediction block. Thetransition zone may occur at a boundary between samples of theprediction block associated with the different PUs of the CU. Forexample, the transition zone may occur at a boundary between samples ofthe prediction block associated with a first PU of the CU and samples ofthe prediction block associated with a second PU. A sample of theprediction block may be associated with a PU when the sample is within aprediction area specified by a prediction tree of the PU.

In some examples, smoothing a sample in the transition area may reducedifferences between the sample and samples that neighbor the sample. Asdescribed elsewhere in this disclosure, smoothing unit 608 may improvecoding efficiency and/or a visual appearance of the decoded sample blockof the CU by performing the smoothing operation. In various examples,smoothing unit 608 performs various smoothing operations. FIGS. 12-18and 20, described in detail elsewhere in this disclosure, illustrateexample operations performed by smoothing unit 608 to adaptively smoothsamples in the transition zones of prediction blocks.

In addition, TU generation unit 606 may perform a transform selectionoperation to select sizes of TUs for the CU (808). As discussed above,transform module 312 may receive residual data for the CU. Transformmodule 312 may then perform a transform operation on each TU of the CU.When transform module 312 performs the transform operation on a TU,transform module 312 may apply a transform to samples of the residualdata that correspond to the TU, thereby generating a transformcoefficient block for the TU. Samples of the residual data maycorrespond to a TU when the samples are within the transform areaspecified by the transform tree of the TU. Inverse transform unit 320 inencoding unit 104 and inverse transform module 710 in decoding unit 106also use transforms having the selected transform sizes whentransforming transform coefficient blocks into sample blocks.

Furthermore, inter-prediction unit 304 may generate inter-predictionsyntax elements (810). The inter-prediction syntax elements may provideinformation about the CU and the prediction block. For example, theinter-prediction syntax elements may include syntax elements thatindicate whether the CU has more than one PU. In this example, if the CUhas more than one PU, the inter-prediction syntax elements may alsoindicate sizes, shapes, and/or locations of the prediction areas of thePUs. In another example, the inter-prediction syntax elements mayspecify inter-prediction modes for the PUs of the CU. Furthermore, theinter-prediction syntax elements may include data based on motionvectors for one or more of the PUs of the CU. In another example, theinter-prediction syntax elements may indicate sizes and/or location ofTUs of the CU. In some examples, the set of inter-prediction syntaxelements includes some or all of the inter-prediction syntax elementsspecified by the H.264 MPEG Part 10 standard.

If mode select unit 302 selects the prediction block generated in step804, inter-prediction unit 304 may output the prediction block toresidual generation unit 310 and reconstruction unit 322. Furthermore,if mode select unit 302 selects the prediction block generated in step804, mode select unit 302 may include the inter-prediction syntaxelements in the coding node, prediction tree, and/or transform tree ofthe CU.

FIG. 9 is a conceptual diagram that illustrates example rectangularpartitioning modes. As explained briefly above, motion estimation unit602 may generate one or more PUs for a CU. Each of the PUs may have aprediction tree that specifies a size and a position of a predictionarea. Each of the prediction areas may correspond to a differentpartition of the sample block of the CU. For ease of explanation, thisdisclosure may explain that a PU corresponds to a partition of thesample block of a CU when the prediction area specified by theprediction tree of the PU corresponds to the partition of the sampleblock of the CU.

In various examples, motion estimation unit 602 may use variouspartitioning modes to generate the PUs of the CU. Such partitioningmodes may include rectangular partitioning modes. In rectangularpartitioning modes, the PUs correspond to rectangular-shaped partitionsof the sample block of the CU. In some examples, motion estimation unit602 is able to use some or all rectangular partitioning modes defined inthe H.264 MPEG Part 10 standard.

The example of FIG. 9 illustrates rectangular partitioning modes 900A-H(collectively, “rectangular partitioning modes 900”). In rectangularpartitioning mode 900A, motion estimation unit 602 generates a single PUfor the CU. The prediction area of this PU is the same size as thesample block of the CU. In rectangular partitioning mode 900B, motionestimation unit 602 generates four PUs for the CU. The PUs generatedusing rectangular partitioning mode 900B correspond to fourequally-sized partitions of the sample block of the CU.

In rectangular partitioning modes 900C through 900H, motion estimationunit 602 generates two PUs for the CU. The PUs generated usingrectangular partitioning mode 900C correspond to equally-sized,horizontally-divided partitions of the sample block of the CU. The PUsgenerated using rectangular partitioning mode 900D correspond toequally-sized, vertically-divided partitions of the sample block of theCU.

The PUs generated using rectangular partitioning mode 900E correspond tohorizontally-divided partitions of the sample block in which the lowerpartition is larger than the upper partition. In some examples, motionestimation unit 602 may partition the sample block horizontally at anysample in the sample block above a horizontal midline of the sampleblock. The PUs generated using rectangular partitioning mode 900Fcorrespond to horizontally-divided partitions of the sample block inwhich the lower partition is smaller than the upper partition. In someexamples, motion estimation unit 602 may partition the sample blockhorizontally at any sample in the sample block below a horizontalmidline of the sample block.

The PUs generated using rectangular partitioning mode 900G correspond tovertically-divided partitions of the sample block in which the leftpartition is smaller than the right partition. In some examples, motionestimation unit 602 may partition the sample block vertically at anysample in the sample block to the left of a vertical midline of thesample block. The PUs generated using rectangular partitioning mode 900Hcorrespond to vertically-divided partitions of the sample block in whichthe left partition is larger than the right partition. In some examples,motion estimation unit 602 may partition the sample block vertically atany sample in the sample block to the right of a vertical midline of thesample block.

FIG. 10 is a conceptual diagram that illustrates example geometricpartitioning modes. In some examples, motion estimation unit 602 uses ageometric partitioning mode to generate two PUs for a CU. When motionestimation unit 602 uses a geometric partitioning mode to generate thePUs for the CU, the PUs correspond to partitions of the sample block ofthe CU whose boundaries do not necessarily meet the edges of the sampleblock at right angles.

In the example of FIG. 10, motion estimation unit 602 has used ageometric partitioning mode to partition a sample block 1000 into afirst partition 1002 and a second partition 1004. A partitioning line1006 separates first partition 1002 and second partition 1004. For easeof explanation, FIG. 10 illustrates a vertical midline 1008 and ahorizontal midline 1010 of sample block 1000. Two parameters define thegeometric partitioning mode used to partition sample block 1000. In thisdisclosure, these two parameters are referred to as theta and rho. Thetheta parameter indicates an angle 1012 at which a line 1014 extendsfrom a central point of sample block 1000. The rho parameter indicates alength 1016 of line 1014. Thus, the theta and rho parameters act aspolar coordinates to indicate a point 1018 within sample block 1000.Partitioning line 1006 is defined such that partitioning line 1006 meetsline 1014 at a right angle. In the example of FIG. 10, an angle 1020 atwhich partitioning line 1006 meets an edge of sample block 1000 is not aright angle. In this way, the theta and rho parameters define thelocation of partitioning line 1006. By using various values for thetheta and rho parameters, motion estimation unit 602 may define variouslines that partition sample block 1000.

Partitioning line 1006 and line 1014 are not necessarily visuallypresent in sample block 1000 and are shown in FIG. 10 to illustrate howsample block 1000 may be geometrically partitioned.

FIG. 11 is a conceptual diagram that illustrates a transition zone 1200of a prediction block 1202 of a CU. In the example of FIG. 11,prediction block 1202 includes samples 1204 associated with a first PUof the CU and samples 1206 associated with a second PU of the CU. Apartitioning line 1208 separates samples 1204 from samples 1206.Transition zone 1200 is a portion of prediction block 1202 occurring ata boundary between samples 1204 and samples 1206. In the example of FIG.11, samples in transition zone 1200 are shown as blocks.

FIGS. 12-18 and 20, described in detail below, illustrate exampleoperations to adaptively smooth samples in transition zones ofprediction blocks. In each of these example operations, smoothing unit608 determines whether to perform a smoothing operation on samples inthe transition zone. In some circumstances, the coding efficiency of aCU may be enhanced by smoothing samples in the transition zone. In othercircumstances, the coding efficiency of the CU may be decreased bysmoothing samples in the transition zone. Thus, by adaptively performinga smoothing operation on samples in the transition zone, encoding unit104 may enhance the coding efficiency of the CU.

FIG. 12 is a flowchart that illustrates an example operation 1250 toadaptively smooth samples in a transition zone of a prediction block.Smoothing unit 608 may perform operation 1250 as an alternative to theexample operations illustrated in FIGS. 13-18 and 20.

As discussed above, residual generation unit 310 may subtract samples ofthe prediction block from samples of the CU to generate residual data.Inter-prediction unit 304 may generate one or more TUs for the CU. Eachof the TUs specifies a transform area. The residual data of the CU ispartitioned among the TUs. For ease of explanation, this disclosure maydescribe portions of the residual data of the CU within the transformarea of a TU as being within the TU. For each TU, transform module 312applies a transform to portions of the residual data of the CU in the TUto generate a transform coefficient block for the TU.

A TU of a CU may span two or more PUs of the CU when the transform areaof the TU overlaps with the prediction areas of the PUs. When a TU of aCU spans two or more PUs of the CU, it is more likely that there aresharp discontinuities in the color or brightness between samples onopposite sides of the partition line between the PUs. In some examples,transform module 312 generates a transform coefficient block having moresignificant (i.e., non-zero) coefficients when transform module 312applies transforms to TUs having samples with sharp discontinuities thanwhen transform module 312 applies transforms to TUs that do not havesamples with sharp discontinuities.

Applying a smoothing filter to samples in the transition zone may reducethe discontinuities in the color or brightness between samples onopposite sides of the partition line between portions of the predictionblock associated with different PUs. Because the discontinuities may bereduced by applying the smoothing filter, transform module 312 maygenerate a transform coefficient block having fewer significantcoefficients.

However, if the TU occurs entirely within one PU, applying a smoothingfilter to samples in the TU may increase the differences between theprediction block of the CU and the original sample block of the CU,resulting in more complex residual data. Transform module 312 maygenerate a transform coefficient block having more significantcoefficients for more complex residual data.

After smoothing unit 608 starts operation 1250, smoothing unit 608determines whether a TU of the CU spans multiple PUs of the CU (1252).If smoothing unit 608 determines that a TU of the CU spans multiple PUsof the CU, smoothing unit 608 makes the determination to perform thesmoothing operation on samples in the transition zone of the predictionblock of the CU. Thus, if smoothing unit 608 determines that a TU of theCU spans multiple PUs of the CU (“YES” of 1252), smoothing unit 608identifies samples in a transition zone of the prediction block of theCU (1254) and performs the smoothing operation on the samples in thetransition zone (1256). Otherwise, if smoothing unit 608 determines thata TU does not span multiple PUs (“NO” of 1252), smoothing unit 608 doesnot perform the smoothing operation on samples in the transition zone(1258).

In various examples, smoothing unit 608 identifies the samples in thetransition zone in various ways. For example, smoothing unit 608 may usethe example transition sample identification operation illustrated inFIG. 20 or the example transition sample identification operationillustrated in FIG. 21 to identify samples in the transition zone.

In various examples, smoothing unit 608 may perform various smoothingoperations on the samples in the transition zone. For example, smoothingunit 608 may perform an Overlapped-Block Motion Compensation (OBMC)smoothing operation on the samples in the transition zone. In an OBMCsmoothing operation, smoothing unit 608 generates two predictions foreach of the samples in the transition zone. Smoothing unit 608 generatesthe first prediction for a given sample by identifying a sample of areference frame indicated by a motion vector of one of the PUs.Smoothing unit 608 generates the second prediction for the given sampleby identifying a sample of the reference frame indicated by a motionvector of the other one of the PUs. If one of the PUs does not have amotion vector, smoothing unit 608 may generate a prediction byidentifying a sample of the reference frame at the same spatiallocations as the given sample.

Furthermore, in an OBMC smoothing operation, smoothing unit 608 blendsthe predictions for each sample in the transition zone. In variousexamples, smoothing unit 608 blends the predictions for a given samplein various ways. For example, smoothing unit 608 may blend thepredictions for a given sample by calculating a weighted average of thepredictions. In various examples, smoothing unit 608 may give differentweights to the predictions. For example, smoothing unit 608 may give aweight of ¾ to the prediction based on the motion vector of the PUcontaining a given sample and may give a weight of ¼ to the predictionbased on the motion vector of the other PU.

In other examples, smoothing unit 608 may perform a filter-basedsmoothing operation. When smoothing unit 608 performs a filter-basedsmoothing operation, smoothing unit 608 may apply one or more filters tothe samples in the transition zone. In such filter-based smoothingoperations, smoothing unit 608 may apply various smoothing filters tothe identified samples in the transition zone. In this example,smoothing unit 608 may apply the following 3×3 smoothing filter to eachsample in the transition zone:

1/15 2/15 1/15 2/15 3/15 2/15 1/15 2/15 1/15

In this example, when smoothing unit 608 applies the smoothing filter toa given sample, 1/15 of the new value of the given sample is based onthe sample above and to the left of the given sample, 2/15 of the newvalue is based on the sample above the given sample, 1/15 of the newvalue is based on the sample above and to the right of the given sample,2/15 of the new value is based on the sample to the left of the givensample, 3/15 of the new value is based on a current value of the givensample, 2/15 of the new value is based on the sample to the right of thegiven sample, 1/15 of the new value is based on the sample below and tothe left of the given sample, 2/15 of the new value is based on thesample below the given sample, and 1/15 of the new value is based on thesample below and to the right of the given sample. Smoothing unit 608may apply other smoothing filters to the samples in the transition zone.For example, smoothing unit 608 may apply smoothing filters withdifferent weights. In another example, smoothing unit 608 may applysmoothing filters of other sizes, e.g., 5×5, 7×7, etc.

In some examples, operation 1250 is part of a method for coding videodata. The method comprises generating a prediction block for a CU in aframe of the video data. The method also comprises determining, by acomputing device, based on whether a TU of the CU spans multiple PUs ofthe CU whether to perform a smoothing operation on samples a transitionzone of the prediction block. The transition zone is at a boundarybetween samples of the prediction block associated with a first PU ofthe CU and samples of the prediction block associated with a second PUof the CU. The method also comprises after the computing device makes adetermination to perform the smoothing operation on the samples in thetransition zone, performing the smoothing operation to smooth thesamples in the transition zone of the prediction block.

In some examples, smoothing unit 608 may make the determination whetherto perform the smoothing operation based on whether a TU spans multiplePUs and based on other factors. For example, smoothing unit 608 may makethe determination whether to perform the smoothing operation based onwhether a TU spans multiple PUs and based on the size of a PU of the CU.

FIG. 13 is a flowchart that illustrates another example operation 1300to adaptively smooth samples in a transition zone of a prediction block.Smoothing unit 608 may perform the operation 1300 as an alternative toperforming the example operations illustrated in FIGS. 12, 14-18, and20.

Smoothing unit 608 may perform operation 1300 to determine whether toperform a smoothing operation on samples in a transition zone of aprediction block. In operation 1300, smoothing unit 608 makes adetermination whether to perform a smoothing operation on samples in thetransition zone based on the sizes of the PUs of a CU. In someinstances, the PUs of a CU may be relatively small. In such instances,smoothing samples in the transition zone between the PUs may not improveimage quality or significantly improve coding efficiency.

After smoothing unit 608 starts operation 1300, smoothing unit 608determines whether a size of one of the PUs is below a given threshold(1302). In some examples, the size of a PU is the size of a predictionarea specified by a prediction tree of the PU. In various examples,smoothing unit 608 may determine whether the size of the PU is belowvarious thresholds. For example, if a PU has size M×N, smoothing unit608 may determine whether either of M or N is smaller than 4. In someexamples, the given threshold is preconfigured into encoding unit 104and decoding unit 106. In other examples, encoding unit 104 and decodingunit 106 programmatically determine the given threshold.

In various examples, smoothing unit 608 may determine whether the sizeof one of the PUs is below the given threshold in various ways. Forexample, encoding unit 104 may store a lookup table. The lookup tablecontains entries that correspond to different PU sizes. Each of theentries indicates whether the corresponding PU size is above or belowthe given threshold.

If the sizes of the PUs are not below the given threshold (“NO” of1302), smoothing unit 608 makes the determination to perform thesmoothing operation on samples in the transition zone. For example, if aPU has size M×N, smoothing unit 608 may make the determination toperform the smoothing operation on the samples in the transition zonewhen either M or N is four or greater. Hence, if the sizes of the PUsare not below the given threshold (“NO” of 1302), smoothing unit 608identifies the samples in the transition zone (1304) and then performsthe smoothing operation on the samples in the transition zone (1306).Otherwise, if the size of one of the PUs is below the given threshold(“YES” of 1302), smoothing unit 608 makes the determination not toperform the smoothing operation on samples in the transition zone(1308). In various examples, smoothing unit 608 may identify the samplesin the transition zone in various ways. For example, smoothing unit 608may use the example operations illustrated in FIGS. 19 and 21 toidentify the samples in the transition zone. Furthermore, in variousexamples, smoothing unit 608 may perform various smoothing operations onthe samples in the transition zone. For example, smoothing unit 608 mayperform one or more OBMC or filter-based smoothing operations asdescribed above with regard to FIG. 12.

In other examples, smoothing unit 608 may perform a version of operation1300 in which smoothing unit 608 determines whether a size of atransform is below the threshold instead of determining whether a sizeof the PU is below the threshold. Transform module 312 may use thetransform when converting residual data based on the prediction blockfrom a spatial domain to a frequency domain. Thus, smoothing unit 608may determine whether to perform the smoothing operation on the samplesin the transition zone based on the size of the transform.

In some examples, operation 1300 is part of a method for coding videodata. The method comprises generating a prediction block for a CU in aframe of the video data. The method also comprises determining, by acomputing device, based on a size of a PU of the CU or based on a sizeof a transform, whether to perform a smoothing operation on samples atransition zone of the prediction block. The transition zone is at aboundary between samples of the prediction block associated with a firstPU of the CU and samples of the prediction block associated with asecond PU of the CU. The method also comprises after the computingdevice makes a determination to perform the smoothing operation on thesamples in the transition zone, performing the smoothing operation tosmooth the samples in the transition zone of the prediction block.

In some examples, smoothing unit 608 may make the determination whetherto perform the smoothing operation based on the size of the PU ortransform and based on other factors. For example, smoothing unit 608may make the determination whether to perform the smoothing operationbased on the size of the PU or transform and based on motion vectors ofPUs of the CU.

FIG. 14 is a flowchart that illustrates another example operation 1400to adaptively smooth samples in a transition zone of a prediction block.Smoothing unit 608 may perform the operation 1400 as an alternative toperforming the example operations illustrated in FIGS. 12, 13, 15-18,and 20.

When motion estimation unit 602 performs the motion estimation operationon the PUs of a CU, motion estimation unit 602 may generate a motionvector for each of the PUs. For example, if the CU has a first PU and asecond PU, motion estimation unit 602 may generate a first motion vectorand a second motion vector. The first motion vector indicates adifference in spatial location between the first PU and a firstreference sample. The first reference sample is an area corresponding tothe first PU within a reference frame. The second motion vectorindicates a difference in spatial location between the second partitionunit and a second reference sample. The second reference sample is anarea corresponding to the second PU within the reference frame.

After smoothing unit 608 starts operation 1400, smoothing unit 608receives motion vectors for PUs of a CU (1402). Smoothing unit 608 thenapplies a function to the motion vectors for the PUs (1404). Thefunction produces a result value. In various examples, smoothing unit608 may apply various functions to the motion vectors. For example,smoothing unit 608 may apply a function that calculates the absolutevalue of subtracting one of the motion vectors from the other motionvector. In another example, smoothing unit 608 may apply a functionbased on an amplitude of a motion vector determined for a neighboring PUand a predicted motion vector. In this example, the predicted motionvector may be predicted from one or more of: the motion vectordetermined for the neighboring prediction unit and a motion vectordetermined for a prediction unit co-located in the reference frame

After applying the function to the motion vectors, smoothing unit 608determines whether the result value exceeds a threshold (1406). Invarious examples, smoothing unit 608 may determine whether the resultvalue exceeds various thresholds. For example, if the result is anabsolute value of a difference between motion vectors, smoothing unit608 may determine whether the result value is greater than thethreshold. In this example, the threshold may be 1 integer pixel. Insome examples, the threshold is a pre-determined parameter that is setat both encoding unit 104 and decoding unit 106. In other examples,encoding unit 104 may generate a syntax element that specifies thethreshold and provide the syntax element to decoding unit 106. Forinstance, encoding unit 104 may provide the threshold to decoding unit106 as a syntax element in a sequence parameter set associated with theCU.

If the result value of the function exceeds the given threshold (“YES”of 1406), smoothing unit 608 makes the determination to perform thesmoothing operation on samples in the transition zone. Hence, if theresult value of the function exceeds the given threshold (“YES” of1406), smoothing unit 608 identifies the samples in the transition zone(1408) and performs the smoothing operation on the samples in thetransition zone (1410). Otherwise, if the result value of the functiondoes not exceed the given threshold (“NO” of 1406), smoothing unit 608does not perform the smoothing operation on samples in the transitionzone (1412). In this way, smoothing unit 608 determines whether toperform the smoothing operation on samples in the transition zone basedat least in part on a first motion vector and a second motion vector. Invarious examples, smoothing unit 608 may identify the samples in thetransition zone in various ways. For example, smoothing unit 608 may usethe example operations illustrated in FIGS. 19 and 21 to identify thesamples in the transition zone. Furthermore, in various examples,smoothing unit 608 may perform various smoothing operations on thesamples in the transition zone. For example, smoothing unit 608 mayperform one or more OBMC or filter-based smoothing operations asdescribed above with regard to FIG. 12.

In some examples, smoothing unit 608 may make the determination whetherto perform the smoothing operation based on the motion vectors and basedon other factors. For example, smoothing unit 608 may make thedetermination whether to perform the smoothing operation based on themotion vectors and based on the size of a PU of the CU. For instance,smoothing unit 608 may make the determination to perform the smoothingoperation when the difference between the motion vectors is greater thana given threshold and the size of a PU in the CU exceeds a threshold.

FIG. 15 is a flowchart illustrating another example operation 1500 toadaptively smooth samples in a transition zone of a prediction block.Inter-prediction unit 304 may perform smoothing determination operation1500 as an alternative to performing the example operations illustratedFIGS. 12-14, 16-18, and 20. In the example of FIG. 15, smoothing unit608 determines whether to perform a smoothing operation on samples in atransition zone of residual data of a given CU based on inter-predictiondirectionalities of PUs of the given CU. The inter-predictiondirectionality of a PU may determine whether the portion of theprediction block of a CU associated with the PU is based on referencesamples from one or two reference frames.

Encoding unit 104 may encode a frame as an intra-frame (i.e., an“I-frame”), a prediction frame (i.e., a “P-frame”), or a bi-directionalprediction frame (i.e., a “B-frame”). Similarly, encoding unit 104 mayencode a slice of a frame as an intra-slice (i.e., an “I-slice”), aprediction slice (i.e., a “P-slice”), or a bi-directional slice (i.e., a“B-slice”). If encoding unit 104 encodes the frame or slice as anI-frame or an I-slice, PUs within the frame or slice may only beintra-predicted PUs. If encoding unit 104 encodes a frame or slice as aP-frame or a P-slice, PUs within the frame or slice may beintra-predicted PUs or uni-predicted PUs. If encoding unit 104 encodes aframe or slice as a B-frame or a B-slice, PUs within the frame or slicemay be intra-predicted PUs, un-predicted PUs, or bi-predicted PUs.Motion compensation unit 604 and/or motion compensation unit 702 maygenerate portions of the prediction block that are associated with anintra-predicted PU without reference to other frames or slices. Motioncompensation unit 604 and/or motion compensation unit 702 may generateportions of the prediction block that are associated with auni-predicted PU based on a single reference frame in the video data. Insome instances, a CU that has only uni-predicted PUs may be referred toas a uni-predicted or uni-prediction CU. Motion compensation unit 604and/or motion compensation unit 702 may generate portions of theprediction block that are associated with a bi-predicted PU based on tworeference frames. In some instances, a CU that includes one or morebi-predicted PUs may be referred to as a bi-predicted or bi-predictionCU.

For example, when a given PU is a uni-predicted PU, encoding unit 104searches a single reference frame for reference samples for the givenPU. This single reference frame may temporally occur before or after theframe of the given PU. In other words, the single reference frame may bein LIST_0 or in LIST_1. Encoding unit 104 may then use the referencesamples of the given PU to generate the portions of a prediction blockfor a CU that are associated with the given PU.

When a given PU is a bi-predicted PU, encoding unit 104 may search tworeference frames to obtain two reference samples for the given PU. Oneof the reference frames may temporally occur before the frame of thegiven PU and one of the reference frames may temporally occur after theframe of the given PU. To generate the prediction block of a CU,encoding unit 104 may perform a blending operation with respect to thegiven PU. When encoding unit 104 performs the blending operation withrespect to the given PU, encoding unit 104 may generate portions of theprediction block that are associated with the given PU based on the tworeference samples of the given PU. For instance, encoding unit 104 maygenerate portions of the prediction block that are associated with thegiven PU based on a weighted average of samples in the two referencesamples of the given PU.

Comparatively more data may need to be retrieved from memory to performan OBMC smoothing operation on samples in the prediction block of a CUthat includes a bi-predicted PU (i.e., a bi-prediction CU) than toperform the OBMC smoothing operation on samples in the prediction blockof a CU that does not include a bi-predicted PU. Hence, refraining fromperforming the smoothing operation when the CU includes a bi-predictedPU may decrease the amount of data retrieved from memory and acceleratethe encoding or decoding process.

As illustrated in the example of FIG. 15, smoothing unit 608 maydetermine whether a CU has a bi-predicted PU (1502). If the CU does nothave a bi-predicted PU (“NO” of 1502), smoothing unit 608 makes thedetermination to perform the smoothing operation on samples of thetransition zone of the prediction block of the CU. Hence, if the CU doesnot have a bi-predicted PU (“NO” of 1502), smoothing unit 608 identifiesthe samples in the transition zone (1504) and performs the smoothingoperation on the samples in the transition zone (1506). In otherexamples, smoothing unit 608 may make the determination to perform thesmoothing operation on the samples in the transition zone when the CUhas a bi-predicted PU. In another example, the CU may have two PUs andone of the PUs is a bi-predicted PU. To perform an OBMC smoothingoperation, smoothing unit 608 may need to derive a subset of samples inthe transition regions using the two reference samples of thebi-predicted PU (which would increase the amount of data retrieved frommemory as explained before). Hence, in this example, smoothing unit 608may make the determination not to perform the OBMC smoothing operationon the subset of samples of the transition region that are associatedwith the bi-predicted PU. Smoothing unit 608 may also make thedetermination to perform the OBMC smoothing operation on other samplesof the transition region that are associated with the other PU based onwhether the other PU is a bi-predicted PU.

In various examples, smoothing unit 608 may identify the samples in thetransition zone in various ways. For example, smoothing unit 608 may usethe example operations illustrated in FIGS. 19 and 21 to identify thesamples in the transition zone. Furthermore, in various examples,smoothing unit 608 may perform various smoothing operations on thesamples in the transition zone. For example, smoothing unit 608 mayperform one or more OBMC or filter-based smoothing operations asdescribed above with regard to FIG. 12.

Otherwise, if the CU has a bi-predicted PU (“YES” of 1502), smoothingunit 608 does not perform the smoothing operation on samples in thetransition zone of the prediction block of the CU (1508). Thus, if theCU has a bi-predicted PU, smoothing unit 608 does not apply thesmoothing operation to samples in the transition zone of the predictionblock. In this way, smoothing unit 608 makes the determination whetherto perform the smoothing operation based on the inter-predictiondirectionality of PUs of the CU, and hence the inter-predictiondirectionalities of the CU.

In some examples, smoothing unit 608 may make the determination whetherto perform the smoothing operation based on the inter-predictiondirectionalities of the PUs of a CU and based on other factors. Forexample, smoothing unit 608 may make the determination whether toperform the smoothing operation based on the inter-predictiondirectionalities of the PUs and based on the size of a PU of the CU. Forinstance, smoothing unit 608 may make the determination to perform thesmoothing operation when the CU does not have a bi-prediction PU and thesize of a PU in the CU exceeds a threshold.

FIG. 16 is a flowchart illustrating another example operation 1600 toadaptively smooth samples in a transition zone of a prediction block.Smoothing unit 608 may perform operation 1600 as an alternative toperforming the operations illustrated in the examples of FIGS. 12-15,17, 18, and 20.

When inter-prediction unit 304 performs operation 1600, smoothing unit608 identifies samples in the transition zone of a prediction block of aCU (1602). In various examples, smoothing unit 608 may identify thesamples in the transition zone in various ways. For example, smoothingunit 608 may use the example operations illustrated in FIGS. 19 and 21to identify the samples in the transition zone.

After identifying the samples in the transition zone, smoothing unit 608determines an amount of difference between samples in the transitionzone that are associated with different PUs of the CU (1604). Forexample, smoothing unit 608 may determine an amount of differencebetween samples in the transition zone that are associated with a firstPU of the CU and samples in the transition zone that are associated witha second PU of the CU.

Smoothing unit 608 then determines whether the amount of differencebetween the samples on opposite sides of the partitioning line exceeds agiven threshold (1606). In different examples, smoothing unit 608determines whether the amount of difference exceeds various thresholds.For example, smoothing unit 608 may determine whether the amount ofdifference between corresponding samples on opposite sides of thepartitioning line is larger than two. In some examples, human users mayconfigure the given threshold. In other examples, encoding unit 104 anddecoding unit 106 determine the given threshold programmatically.

If the difference between samples on opposite sides of the partitioningline exceeds the given threshold (“YES” of 1606), smoothing unit 608 mayperform a smoothing operation on samples in the transition zone (1608).For example, smoothing unit 608 may perform a smoothing operation on thesamples in the transition zone if the difference between correspondingsamples on opposite sides of the partitioning line is two or less. Invarious examples, smoothing unit 608 may perform various smoothingoperations on the samples. For example, smoothing unit 608 may performan OBMC-based or a filter-based smoothing operation on samples in thetransition zone as described above with regard to FIG. 12. Otherwise, ifthe difference between samples in opposites sides of the partitioningline does not exceed the given threshold (“NO” of 1606), smoothing unit608 does not perform the smoothing operation on the samples in thetransition zone (1610). In this way, smoothing unit 608 may determinewhether to perform the smoothing operation based at least in part on anamount of difference between samples in the transition zone that areassociated with one PU of the CU and samples in the transition zone thatare associated with another PU of the CU.

In some examples, smoothing unit 608 may make the determination whetherto perform the smoothing operation based on the amount of differencebetween samples in the transition zone that are associated withdifferent PUs and based on other factors. For example, smoothing unit608 may make the determination whether to perform the smoothingoperation based on the amount of difference between samples in thetransition zone that are associated with different PUs and based on thesize of a PU of the CU.

FIG. 17 is a flowchart that illustrates another example operation 1700to adaptively smooth samples in a transition zone of a prediction block.Smoothing unit 608 may perform operation 1700 as an alternative toperforming the example operations illustrated in FIGS. 12-16, 18, and20. When inter-prediction unit 304 performs operation 1700,inter-prediction unit 304 may perform smoothing operations on samples inthe transition zone selectively. In other words, inter-prediction unit304 does not necessarily smooth all samples in the transition zone.

When inter-prediction unit 304 performs operation 1700, smoothing unit608 performs a transition sample identification operation to identifysamples in a transition zone of a prediction block of a CU (1702). Invarious examples, smoothing unit 608 may perform various transitionsample identification operations. For example, smoothing unit 608 mayperform the example transition sample identification operationillustrated in FIG. 19 or the example transition sample identificationoperation illustrated in FIG. 21.

After identifying the samples in the transition zone, smoothing unit 608determines whether there are remaining samples in the transition zone(1704). If there are one or more remaining samples in the transitionzone (“YES” of 1704), smoothing unit 608 selects one of the remainingsamples in the transition zone (1706). After smoothing unit 608 selectsthe sample, smoothing unit 608 may not consider the sample to be aremaining sample.

Smoothing unit 608 then determines whether the selected sample satisfiesparticular criteria (1708). If the selected sample satisfies thecriteria (“YES” of 1708), smoothing unit 608 performs a smoothingoperation on the selected sample (1710). For example, smoothing unit 608may perform an OBMC-based or a filter-based smoothing operation on theselected sample. If the selected sample does not satisfy the criteria(“NO” of 1708), smoothing unit 608 does not perform the smoothingoperation on the selected sample (1712). In either case, smoothing unit608 again determines whether there are remaining samples in thetransition zone (1704). If there are remaining samples in the transitionzone, smoothing unit 608 may repeat steps 1706, 1708, 1710, and 1712with regard to the remaining samples.

In various examples, smoothing unit 608 may determine whether theselected sample satisfies various criteria. For example, smoothing unit608 may determine that the selected sample satisfies the criteria if theselected sample is in a given PU of CU. In this example, smoothing unit608 may determine that the selected sample does not satisfy the criteriaif the selected sample is in another PU of the CU. In this way,smoothing unit 608 may perform a smoothing operation on samples in thegiven PU, but not samples in the other PU. For instance, when smoothingunit 608 performs the smoothing operation, smoothing unit 608 modifiessamples in the transition zone that are associated with a first PU ofthe CU and does not modify samples in the transition zone that areassociated with a second PU of the CU.

In another example, smoothing unit 608 may receive a sequence parameterset (SPS). In this example, smoothing unit 608 may determine that asample satisfies the criteria if the SPS specifies the samples. In thisexample, smoothing unit 608 may determine that the sample does notsatisfy the criteria if the SPS does not specify the sample.

FIG. 18 is a flowchart illustrating another example operation 1800 toadaptively smooth samples in a transition zone of a prediction block.Inter-prediction unit 304 may perform operation 1800 as an alternativeto performing the example operations illustrated in FIGS. 12-17 and 20.In the example of FIG. 18, smoothing unit 608 adaptively chooses asmoothing operation from among a plurality of available smoothingoperations. In some examples, adaptively choosing a smoothing operationfrom among multiple available smoothing operations may result in greatercoding efficiency and/or image quality.

After smoothing unit 608 starts operation 1800, smoothing unit 608determines whether to perform a first smoothing operation on samples inthe transition zone of a prediction block of a CU (1802). If smoothingunit 608 makes the determination to perform the first smoothingoperation on the samples in the transition zone (“YES” of 1802),smoothing unit 608 may identify the samples in the transition zone(1804) and then perform the first smoothing operation on the samples inthe transition zone (1806). In various examples, smoothing unit 608 maymake the determination to perform the first smoothing operation invarious ways. For example, smoothing unit 608 may use the exampleoperations illustrated in FIGS. 12-17 to make the determination whetherto perform the first smoothing operation. In this example, smoothingunit 608 may make the determination to apply the first smoothingoperation to samples in the transition zone if the size of the CU is32×32 or 16×16.

Otherwise, if smoothing unit 608 makes the determination not to performthe first smoothing operation (“NO” of 1802), smoothing unit 608 maydetermine whether to perform a second smoothing operation on the samplesin the transition zone (1808). If smoothing unit 608 makes thedetermination to perform the second smoothing operation on samples inthe transition zone (“YES” of 1808), smoothing unit 608 may identifysamples in the transition zone (1810) and perform the second smoothingoperation on samples in the transition zone (1812). In various examples,smoothing unit 608 may make the determination to perform the secondsmoothing operation in various ways. For example, smoothing unit 608 mayuse the example operations illustrated in FIGS. 12-17 to make thedetermination to perform the second smoothing operation. In thisexample, smoothing unit 608 may make the determination to apply thesecond smoothing operation to samples in the transition zone if the sizeof the CU is 8×8 and if the inter-prediction direction of the CU is abi-predicted CU. Thus, when smoothing unit 608 performs operation 1800,smoothing unit 608 may determine whether to perform smoothing operationsbased on combinations of the operations described above.

After performing the first smoothing operation, performing the secondsmoothing operation, or after making the determination not to performthe second smoothing operation, smoothing unit 608 may output a syntaxelement that indicates which, if any, smoothing operation was performedon samples in the transition zone (1814). Motion compensation unit 604in encoding unit 104 may use this syntax element when reconstructing theCU as part of a reference frame. In addition, motion compensation unit702 in decoding unit 106 may use this syntax element when reconstructinga prediction block for the CU.

The first and second smoothing operations may be the same or differenttypes of smoothing operations. For example, a first smoothing kernelconfigures the first smoothing operation and second smoothing kernel mayconfigure the second smoothing operation. In this example, the kernelsdefine the type and extent of smoothing performed by smoothing filters.In this example, smoothing unit 608 may make the determination whetherto perform the first smoothing operation based on a size of a transform,a size of a PU of the CU, an amplitude of a motion vector determined fora neighboring PU and a predicted motion vector. In this example, themotion vector predicted from one or more of the motion vectorsdetermined for the neighboring prediction unit and a motion vectordetermined for a prediction unit co-located in the reference frame.

In another example, the first smoothing operation may be an OBMC-basedsmoothing operation and the second smoothing operation may be afilter-based smoothing operation. Because the first and second smoothingoperations may be different types of smoothing operations, the firstsmoothing operation and the second smoothing operation may produceversions of the prediction block that differ from one another.

FIG. 19 is a flowchart that illustrates an example transition sampleidentification operation 2100. Smoothing unit 608 may perform transitionsample identification operation 2100 to identify samples in a transitionzone of a prediction block. After smoothing unit 608 starts transitionsample identification operation 2100, smoothing unit 608 selects thesize of a neighbor region (2102). In other words, smoothing unit 608selects a neighbor region size. In various examples, smoothing unit 608may select various neighbor region sizes. For example, smoothing unit608 may select 3×3, 5×5, 7×7, or another size as the neighbor regionsize.

In various examples, smoothing unit 608 selects the neighbor region sizein various ways. For example, encoding unit 104 and decoding unit 106may store a pre-determined neighbor region size syntax element thatindicates a size. In this example, encoding unit 104 and decoding unit106 read the neighbor region size syntax element and use the neighborregion size syntax element to select the neighbor region size.

In another example, encoding unit 104 stores a neighbor region sizeparameter that indicates a size. In some examples, encoding unit 104and/or decoding unit 106 receives the neighbor region size parameterfrom a human user. In other examples, the neighbor region size parameteris set programmatically. Encoding unit 104 uses the neighbor region sizeparameter to select the neighbor region size. In addition, encoding unit104 signals the neighbor region size to decoding unit 106. For example,encoding unit 104 may output a syntax element in a picture parameter set(PPS) or a slice parameter set (SPS) that indicates the neighbor regionsize.

In another example, encoding unit 104 may, prior to smoothing unit 608performing the transition sample identification operation 2100, selectthe neighbor region size for a frame of multimedia content, a group ofpictures that includes a current frame, another CU, a group of CUs, agroup of sub-CUs, a slice of the current frame, two or more portions ofthe current frame, or another part of multimedia content. In thisexample, encoding unit 104 may select the neighbor region size based oncertain criteria, and explicitly signal the selected neighbor size to adecoder. Smoothing unit 608 selects the neighbor region size based onthe neighbor region size previously selected for the part of multimediacontent associated with the CU.

In yet another example, smoothing unit 608 may select the neighborregion size based on characteristics of the CU. In various examples,smoothing unit 608 may select the neighbor region size based on variouscharacteristics of the CU. For example, smoothing unit 608 may selectthe neighbor region size based on a size of the CU. In another example,smoothing unit 608 may select the neighbor region size based on a motionvector of a PU in the CU. In this example, smoothing unit 608 may selectthe neighbor region size based on an amplitude of the motion vector. Inyet another example, smoothing unit 608 may select the neighbor regionsize based on a prediction mode (i.e., skip mode, direct mode,inter-frame mode, or intra-frame mode) of a PU of the CU. In yet anotherexample, smoothing unit 608 may select the neighbor region size based ondifferences in samples in the prediction block.

In yet another example, smoothing unit 608 may select the neighborregion size based on the results of encoding other CUs in the videodata. For example, smoothing unit 608 may determine that predictionblocks generated for other CUs using a larger neighbor region sizeresult in less distortion than prediction blocks generated using smallerneighbor region sizes, or vice versa. In other examples, the neighborregion size may be determined based on the partition modes of theprevious encoded CU, the partition modes of the previous encoded CUs,the motion vectors of the previous encoded CUs. Based on thisdetermination, smoothing unit 608 may select a neighbor region size thattends to produce prediction blocks with the lowest levels of distortion.

After selecting the neighbor region size, smoothing unit 608 identifiessamples in the transition zone. As described below, the transition zoneincludes a sample associated with a given PU of the CU when a neighborregion contains the sample and also contains a sample of the predictionblock that is associated with another PU of the CU. The neighbor regionhas the selected neighbor region size. To identify the samples in thetransition zone, smoothing unit 608 may determine whether there are anyremaining samples in the prediction block (2104). If there are one ormore remaining samples in the prediction block (“YES” of 2104),smoothing unit 608 selects one of the remaining samples (2106). Aftersmoothing unit 608 selects the sample, smoothing unit 608 does notconsider the sample to be a remaining sample of the prediction block.Smoothing unit 608 then identifies the PU associated with the selectedsample (2108).

Next, smoothing unit 608 identifies samples in a neighbor region (2110).The neighbor region is a zone of the prediction block that has theselected neighbor region size and that contains the selected sample(2110). In some examples, the neighbor region is centered on theselected sample. For example, if the neighbor region is a 3×3 square,the selected sample would be the central sample of the 3×3 square. Inanother example, the neighbor region is not centered on the selectedsample. For example, if the neighbor region is a 3×3 square, theselected sample may be on a top, bottom, left, or right edge of the 3×3square. Thus, if the neighbor region is not centered on the selectedsample, the transition zone may extend further into one PU than theother PU.

In some examples, inter-prediction unit 304 may use smoothing unit 608to perform transition sample identification operation 2100 multipletimes. For example, smoothing unit 608 may perform transition sampleidentification operation 2100 once with neighbor regions centered onselected samples and once with neighbor regions not centered on selectedzones. Inter-prediction unit 304 may evaluate whether the predictionblock has less distortion when the neighbor regions are centered onselected samples or when the neighbor regions are not centered onselected samples. Inter-prediction unit 304 may select the predictionblock having less distortion.

After identifying the samples in the neighbor region, smoothing unit 608determines whether any of the samples in the neighbor region are in adifferent PU than the selected sample (2112). If any of the samples inthe neighbor region are in a different PU than the selected sample(“YES” of 2112), smoothing unit 608 identifies the selected sample asbeing in the transition zone (2114). Otherwise, if none of the samplesin the neighbor region are in a different PU than the selected sample(“NO” of 2112), smoothing unit 608 does not identify the selected sampleas being in the transition zone (2116). In either case, smoothing unit608 may again determine whether there are remaining samples in theprediction block (2104).

If there are remaining samples in the prediction block, smoothing unit608 may repeat steps 2106, 2108, 2110, 2112, 2114, and 2116 with regardto the remaining samples in the prediction block. If there are noremaining samples in the prediction block (“NO” of 2104), smoothing unit608 ends transition sample identification operation 2100.

FIG. 20 is a flowchart that illustrates another example operation 2200of smoothing unit 608. After smoothing unit 608 starts operation 2200,smoothing unit 608 identifies samples in a first transition zone of aprediction block of a CU (2202). In various examples, smoothing unit 608identifies the samples in the first transition zone in various ways. Forexample, smoothing unit 608 may test samples in the prediction block.When smoothing unit 608 tests a given sample, smoothing unit 608 maydetermine whether samples in a neighbor region are associated with adifferent PU of the CU than the given sample. In this example, theneighbor region may have various sizes. For example, the neighbor regionmay be a 3×3 square.

Smoothing unit 608 then identifies samples in a second transition zoneof the prediction block (2204). In various examples, smoothing unit 608identifies the samples in the second transition zone in various ways.For example, smoothing unit 608 may test samples in the predictionblock. In this example, when smoothing unit 608 tests a given sample,smoothing unit 608 determines whether samples in a neighbor region areassociated with a different PU of the CU than the given sample. In thisexample, smoothing unit 608 may identify samples of the prediction blockin a second transition zone, wherein samples associated with a first PUof the CU are in the second transition zone if neighbor regions thatcontain the samples contain samples associated with a second PU of theCU. In this example, samples associated with the second PU are in thesecond transition zone if neighbor regions that contain the samplescontain samples associated with the first PU. In this example, theneighbor region is a zone of the prediction block that includes thegiven sample. The size of the neighbor region may be different than thesize of the neighbor regions used to identify samples in the firsttransition zone. For example, the size of the neighbor region used toidentify samples in the first transition zone may be 3×3 and the size ofthe neighbor region used to identify samples in the second transitionzone may be 5×5.

Smoothing unit 608 then performs a first smoothing operation on samplesin the first transition zone (2206). Smoothing unit 608 performs asecond smoothing operation on samples in the second transition zone(2208). The second smoothing operation may be different than the firstsmoothing operation. For example, the first and second smoothingoperations may both be OBMC-based smoothing operations. However, in thisexample, the OBMC-based smoothing operations may blend predicted samplesdifferently. For example, the OBMC-based smoothing operation may usedifferent weights for predictions from different PUs. For instance, inthis example, if a sample is associated with a first PU of the CU and isalso in the first transition zone, smoothing unit 608 may use a weight ¾for a prediction from the first PU and weight of ¼ for a prediction fromthe second PU of the CU. In this example, if a sample is associated withthe first PU and is also in the second transition zone, smoothing unit608 may use a weight of ⅞ for the prediction from the first PU and aweight of ⅛ for the prediction from the second PU. In another example,one of the smoothing operations may be an OBMC-based smoothing operationand one of the smoothing operations may be a filter-based smoothingoperation. In yet another example, the first and second smoothingoperations may both be filter-based smoothing operations. However, inthis example, the filters used in the first and second smoothingoperations may be different.

In this way, smoothing unit 608 may smooth different parts of theprediction block in different ways. This may result in greater codingefficiency and/or lower amounts of distortion.

FIG. 21 is a flowchart that illustrates another example transitionsample identification operation 2300. Smoothing unit 608 may performtransition sample identification operation 2300 to identify samples in atransition zone of a prediction block of a CU. After smoothing unit 608starts transition sample identification operation 2300, smoothing unit608 determines whether there are any remaining samples in the predictionblock (2302). If there are one or more remaining samples in theprediction block (“YES” of 2304), smoothing unit 608 selects one of theremaining samples (2304). After smoothing unit 608 selects the sample,smoothing unit 608 does not consider the sample to be a remaining sampleof the prediction block. Smoothing unit 608 then identifies a PU of theCU associated with the selected sample (2306).

Next, smoothing unit 608 selects a neighbor region size associated withthe PU associated with the selected sample (2308). Different PUs may beassociated with different neighbor region sizes. For example, a CU mayhave a first PU and a second PU. In this example, the first PU may beassociated with a neighbor region size of 3×3 samples and the secondregion PU may be associated with a neighbor region size of 5×5 samples.In this example, if the selected sample is associated with the first PU,smoothing unit 608 selects the neighbor region size of 3×3 samples. Inthis example, if the selected sample is associated with the second PU,smoothing unit 608 selects the neighbor region size of 5×5 samples.

In various examples, PUs may be associated with neighbor region sizes invarious ways. For example, encoding unit 104 and decoding unit 106 maystore and/or receive data that separately indicates the neighbor regionsizes associated with the PUs. In another example, encoding unit 104 anddecoding unit 106 may store and/or receive data that indicate a neighborregion size associated with a first PU. In this example, encoding unit104 and decoding unit 106 may derive the neighbor region size associatedwith a second PU from the neighbor region size associated with the firstPU.

Smoothing unit 608 then identifies samples in a neighbor region (2310).The neighbor region is a zone of the prediction block that has theselected neighbor region size and that contains the selected sample.Similar to the example transition sample identification operation 2100discussed above, the neighbor region may be centered on the selectedsample or not be centered on the selected sample.

After identifying the samples in the neighbor region, smoothing unit 608determines whether any of the samples in the neighbor region areassociated with a different PU than the selected sample (2312). If anyof the samples in the neighbor region are associated with a different PUthan the selected sample (“YES” of 2312), smoothing unit 608 identifiesthe selected sample as being in the transition zone (2314). Otherwise,if none of the samples in the neighbor region are associated with adifferent PU than the selected sample (“NO” of 2312), smoothing unit 608does not identify the selected sample as being in the transition zone(2316). In either case, smoothing unit 608 may again determine whetherthere are remaining samples in the prediction block (2302).

If there are remaining samples in the prediction block, smoothing unit608 may repeat steps 2304, 2306, 2308, 2310, 2312, 2314, and 2316 withregard to the remaining samples in the prediction block. If there are noremaining samples in the prediction block (“NO” of 2302), smoothing unit608 ends transition sample identification operation 2300.

In some instances, the transition sample identification operation 2300implements a method that comprises generating a prediction block for aCU in a frame of video data, the CU having a first PU and a second PU.The method also comprises selecting a first sample in the predictionblock and determining that the first sample is associated with the firstPU. The method also comprises selecting, by a computing device, a firstneighbor region size by determining that the first PU is associated withthe first neighbor region size. The method also comprises identifyingthe first sample as being within the first transition zone when aneighbor region having the first neighbor region size contains the firstsample and also contains a sample associated with the second PU. Themethod also comprises selecting a second sample in the prediction block,determining that the second sample is associated with the second PU, anddetermining that the second PU is associated with a second neighborregion size. The second neighbor region size is different than the firstneighbor region size. The method also comprises identifying the secondsample as being within the first transition zone when a neighbor regionhaving the second neighbor region size contains the second sample andalso contains a sample associated with the first PU. The method alsocomprises performing a first smoothing operation on the samples in thefirst transition zone.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted as one ormore instructions or code on a computer-readable medium and executed bya hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother, e.g., according to a communication protocol. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that may be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the techniques described inthis disclosure. A computer program product may include acomputer-readable medium.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted as one ormore instructions or code on a computer-readable medium and executed bya hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother, e.g., according to a communication protocol. In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium such as a signal or carrier wave. Data storagemedia may be any available media that may be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the techniques described inthis disclosure. A computer program product may include acomputer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia may comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, or any other medium that may be used to store desired programcode in the form of instructions or data structures and that may beaccessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if instructions are transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. It should be understood, however, thatcomputer-readable storage media and data storage media do not includeconnections, carrier waves, signals, or other transient media, but areinstead directed to non-transient, tangible storage media. Disk anddisc, as used herein, includes compact disc (CD), laser disc, opticaldisc, digital versatile disc (DVD), floppy disk and Blu-ray disc wheredisks usually reproduce data magnetically, while discs reproduce dataoptically with lasers. Combinations of the above should also be includedwithin the scope of computer-readable media.

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto any of the foregoing structure or any other structure suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated hardware and/or software modules configured for encoding anddecoding, or incorporated in a combined codec. Also, the techniquescould be fully implemented in one or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components,modules, or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily require realization by differenthardware units. Rather, as described above, various units may becombined in a codec hardware unit or provided by a collection ofinteroperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Various examples have been described. These and other examples arewithin the scope of the invention defined by the following claims.

What is claimed is:
 1. A method of decoding video data, the methodcomprising: receiving an indication of a coding unit (CU) of video datato be decoded, prediction parameters associated with a first predictionunit (PU) of the CU, a size of a CU, and prediction parametersassociated with a second PU of the CU, wherein the prediction parametersassociated with the first PU include first motion information; receivingone or more parameters indicative of a geometric partitioning modeassociated with the CU, wherein the indicated geometric partitioningmode corresponds to a non-perpendicular partitioning line that separatesfirst samples to be predicted that are associated with the first PU andsecond samples to be predicted that are associated with the second PU;and generating a prediction block for the CU using the indicatedgeometric partitioning mode, comprising: identifying a first predictionblock of samples based on the prediction parameters associated with thefirst PU; identifying a second prediction block of samples based on theprediction parameters associated with the second PU; identifying atransition zone of samples from the first prediction block of samplesand the second prediction block of samples based on thenon-perpendicular partitioning line and the size of the CU, whereinidentifying the transition zone comprises, for each respective sample ofthe first PU: determining whether any sample in a respective2-dimensional region of samples for the respective sample of the firstPU is in the second PU, wherein the respective 2-dimensional region ofsamples for the respective sample of the first PU is centered on therespective sample of the first PU and has a region size determined basedon the size of the CU; and identifying whether the respective sample ofthe first PU is in the transition zone, wherein the respective sample ofthe first PU is identified as being in the transition zone in responseto determining a sample in the respective 2-dimensional region ofsamples for the respective sample of the first PU is in the second PUand the respective sample of the first PU is identified as not being inthe transition zone in response to determining no sample in therespective 2-dimensional region of samples for the respective sample ofthe first PU is in the second PU; identifying a plurality of firstweights and a plurality of second weights corresponding to theidentified transition zone; and for each sample position of theprediction block for the CU to be predicted, applying a weighted sum ofpixel values from the first prediction block of samples and the secondprediction block of samples to produce a predicted sample.
 2. The methodaccording to claim 1, wherein the prediction parameters associated withthe second PU include second motion information, and the appliedweighted sum of pixel values from the first prediction block of samplesand the second prediction block of samples corresponds to a calculatedweighted average of first motion compensated prediction associated withthe first PU and second motion compensated prediction associated withthe second PU.
 3. The method according to claim 1, wherein the appliedweighted sum for each sample position of the prediction block for the CUto be predicted provides a filter-based smoothing operation to the videoprediction samples in the transition zone corresponding to one or morepredetermined filters.
 4. The method according to claim 3, wherein eachof the one or more predetermined filters is a predetermined set ofweights set forth in a predetermined table.
 5. The method according toclaim 3, wherein each of the one or more predetermined filters is apredetermined set of weights set forth in a predetermined table suchthat each of the plurality of weights increases in value with respect todistance from the non-perpendicular partitioning line.
 6. The methodaccording to claim 1, further comprising: receiving an indication forrectangularly partitioning a frame of the video data into a plurality oftreeblocks, wherein the treeblocks are rectangularly partitioned into ahierarchy of progressively smaller sample blocks, said hierarchyrepresenting a hierarchical tree structure; receiving an indication offurther partitioning the progressively smaller sample blocks into leafnodes, with each of the leaf nodes corresponding to a respectiveprediction block for a respective CU of a plurality of CUs; andgenerating a prediction block for each CU of the plurality of CUs. 7.The method according to claim 6, wherein each of the respectiveprediction blocks for each CU of the plurality of CUs may vary in size,further comprising: selectively executing said method if the respectiveprediction block is 8×8.
 8. The method according to claim 6, whereineach of the respective prediction blocks for each CU of the plurality ofCUs may vary in size, further comprising: selectively executing saidmethod if the respective prediction block is 32×32 or 16×16.
 9. Themethod according to claim 6, wherein each of the respective predictionblocks for each CU of the plurality of CUs may vary in size, furthercomprising: selectively executing said method if the respectiveprediction block is 64×64.
 10. The method according to claim 1, whereinfor each sample position of the prediction block for the CU to bepredicted, said applying operation is adaptively executed in instancesto enhance visual quality or compression performance.
 11. The methodaccording to claim 1, for each sample position of the prediction blockfor the CU to be predicted, said applying operation is adaptivelyexecuted based on the size of the CU.
 12. The method according to claim1, wherein the one or more parameters indicative of the geometricpartitioning mode associated with the CU, comprises at least twoparameters such that the non-perpendicular partitioning line isdesignated by the at least two parameters.
 13. The method according toclaim 12, wherein the parameters indicative of the geometricpartitioning mode associated with the CU indicate an angle and define alocation of the non-perpendicular partitioning line.
 14. A computingdevice that decodes video data, the computing device comprising: astorage medium configured to store a reconstructed sample block for acoding unit (CU) of a picture of the video data; and a processor incommunication with the storage medium, wherein the processor isconfigured to: receive an indication of the CU, prediction parametersassociated with a first prediction unit (PU) of the CU, a size of a CU,and prediction parameters associated with a second PU of the CU, whereinthe prediction parameters associated with the first PU include firstmotion information; receive one or more parameters indicative of ageometric partitioning mode associated with the CU, wherein theindicated geometric partitioning mode corresponds to a non-perpendicularpartitioning line that separates first samples to be predicted that areassociated with the first PU and second samples to be predicted that areassociated with the second PU; and generate a prediction block for theCU using the indicated geometric partitioning mode, the processorfurther configured to: identify a first prediction block of samplesbased on the prediction parameters associated with the first PU;identify a second prediction block of samples based on the predictionparameters associated with the second PU; identify a transition zone ofsamples from the first prediction block of samples and the secondprediction block of samples based on the non-perpendicular partitioningline and the size of the CU, wherein the processor is configured suchthat, as part of identifying the transition zone, the processor, foreach respective sample of the first PU: determines whether any sample ina respective 2-dimensional region of samples for the respective sampleof the first PU is in the second PU, wherein the respective2-dimensional region of samples for the respective sample of the firstPU is centered on the respective sample of the first PU and has a regionsize determined based on the size of the CU; and identifies whether therespective sample of the first PU is in the transition zone, wherein therespective sample of the first PU is identified as being in thetransition zone in response to determining a sample in the respective2-dimensional region of samples for the respective sample of the firstPU is in the second PU and the respective sample of the first PU isidentified as not being in the transition zone in response todetermining no sample in the respective 2-dimensional region of samplesfor the respective sample of the first PU is in the second PU; identifya plurality of first weights and a plurality of second weightscorresponding to the identified transition zone; and for each sampleposition of the prediction block for the CU to be predicted, apply aweighed weighted sum of pixel values from the first prediction block ofsamples and the second prediction block of samples to produce apredicted sample; and use reconstructed residual data of the CU and theprediction block for the CU to generate the reconstructed sample blockfor the CU.
 15. The computing device according to claim 14, wherein theprediction parameters associated with the second PU include secondmotion information, and the applied weighted sum of pixel values fromthe first prediction block of samples and the second prediction block ofsamples corresponds to a calculated weighted average of first motioncompensated prediction associated with the first PU and second motioncompensated prediction associated with the second PU.
 16. The computingdevice according to claim 14, wherein the applied weighted sum for eachsample position of the prediction block for the CU to be predictedprovides a filter-based smoothing operation to the video predictionsamples in the transition zone corresponding to one or morepredetermined filters.
 17. The computing device according to claim 16,wherein each of the one or more predetermined filters is a predeterminedset of weights set forth in a predetermined table.
 18. The computingdevice according to claim 16, wherein each of the one or morepredetermined filters is a predetermined set of weights set forth in apredetermined table such that each of the plurality of weights increasesin value with respect to distance from the non-perpendicularpartitioning line.
 19. The computing device according to claim 14, theprocessor further configured to: receive an indication for rectangularlypartitioning a frame of the video data into a plurality of treeblocks,wherein the treeblocks are rectangularly partitioned into a hierarchy ofprogressively smaller sample blocks, said hierarchy representing ahierarchical tree structure; receive an indication of furtherpartitioning the progressively smaller sample blocks into leaf nodes,with each of the leaf nodes corresponding to a respective predictionblock for a respective CU of a plurality of CUs; and generate aprediction block for each CU of the plurality of CUs.
 20. The computingdevice according to claim 19, wherein each of the respective predictionsblocks for each CU of the plurality of CUs may vary in size, theprocessor further configured to: selectively use the indicated geometricpartitioning mode if the respective prediction block is 8×8.
 21. Thecomputing device according to claim 19, wherein each of the respectiveprediction blocks for each CU of the plurality of CUs may vary in size,the processor further configured to: selectively use the indicatedgeometric partitioning mode if the respective prediction block is 32×32or 16×16.
 22. The computing device according to claim 19, wherein eachof the respective prediction blocks for each CU of the plurality of CUsmay vary in size, the processor further configured to: selectively usethe indicated geometric partitioning mode if the respective predictionblock is 64×64.
 23. The computing device according to claim 14, whereinfor each sample position of the prediction block for the CU to bepredicted, the weighted sum of pixel values is adaptively applied ininstances to enhance visual quality or compression performance.
 24. Thecomputing device according to claim 14, for each sample position of theprediction block for the CU to be predicted, the weighted sum of pixelvalues is adaptively applied based on the size of the CU.
 25. Thecomputing device according to claim 14, wherein the one or moreparameters indicative of the geometric partitioning mode associated withthe CU comprises at least two parameters such that the non-perpendicularpartitioning line is designated by the at least two parameters.
 26. Thecomputing device according to claim 25, wherein the at least twoparameters indicative of the geometric partitioning mode associated withthe CU indicate an angle and define a location of the positioning line.27. The computing device according to claim 14, wherein the computingdevice comprises at least one of: an integrated circuit; amicroprocessor; or a wireless communication device.
 28. The computingdevice according to claim 14, further comprising a display configured todisplay the decoded video data.
 29. The computing device according toclaim 14, further comprising a camera configured to capture the videodata.
 30. The computing device according to claim 14, wherein thecomputing device is a wireless communication device, further comprising:a receiver configured to receive a bitstream comprising a sequence ofbits that forms a representation of coded pictures of the video data.31. The computing device according to claim 30, wherein the wirelesscommunication device is a cellular telephone and the bitstream isreceived by the receiver and modulated according to a cellularcommunication standard.
 32. A method of encoding video data, the methodcomprising: receiving a sample block of a coding unit (CU) in a frame ofthe video data; geometrically partitioning the sample block of the CUinto a first area of samples and a second area of samples with respectto a non-perpendicular partitioning line using a geometric partitioningmode, and identifying the non-perpendicular partitioning line by one ormore partitioning line parameters; performing motion estimation withrespect to the first area to generate first prediction parametersassociated with a first prediction unit (PU) of the CU, wherein thefirst prediction parameters include a first motion vector; performingmotion estimation with respect to the second area to generate secondprediction parameters associated with a second prediction unit (PU) ofthe CU; identifying a transition zone of samples from the first area andthe second area based on the non-perpendicular partitioning line and asize of the CU, wherein identifying the transition zone comprises, foreach respective sample of the first PU: determining whether any samplein a respective 2-dimensional region of samples for the respectivesample of the first PU is in the second PU, wherein the respective2-dimensional region of samples for the respective sample of the firstPU is centered on the respective sample of the first PU and has a regionsize determined based on the size of the CU; and identifying whether therespective sample of the first PU is in the transition zone, wherein therespective sample of the first PU is identified as being in thetransition zone in response to determining a sample in the respective2-dimensional region of samples for the respective sample of the firstPU is in the second PU and the respective sample of the first PU isidentified as not being in the transition zone in response todetermining no sample in the respective 2-dimensional region of samplesfor the respective sample of the first PU is in the second PU;identifying a plurality of first weights and a plurality of secondweights corresponding to the identified transition zone; and for eachsample position of the sample block of the CU, determining a first pixelvalue corresponding to the first prediction parameters, determining asecond pixel value corresponding to the second prediction parameters,and applying a weighted sum of the first prediction values and thesecond prediction values of samples to produce a prediction block forthe CU.
 33. The method of encoding according to claim 32, wherein thesecond prediction parameters include a second motion vector, the methodfurther comprising: subtracting the predicted block for the CU from thesample block of the CU to generate residual data; and transmitting thefirst prediction parameters, the second prediction parameters, the firstmotion vector, the second motion vector, the one or more partitioningline parameters, and the residual data as an encoded CU of the videodata.
 34. The method according to claim 32, wherein the secondprediction parameters include second motion information, and thepredicted block for the CU corresponds to a calculated weighted averageof first motion compensated prediction associated with the first PU andsecond motion compensated prediction associated with the second PU. 35.The method according to claim 32, wherein the applied weighted sum foreach sample position of the prediction block for the CU to be predictedprovides a filter-based smoothing operation to the samples in thetransition zone corresponding to one or more predetermined filters. 36.The method according to claim 35, wherein each of the one or morepredetermined filters is a set of predetermined weights set forth in apredetermined table.
 37. The method according to claim 35, wherein eachof the one or more predetermined filters is a set of predeterminedweights set forth in a predetermined table such that each of theplurality of weights increases in value with respect to distance fromthe non-perpendicular partitioning line.
 38. The method according toclaim 32, further comprising: determining an indication forrectangularly partitioning the frame of the video data into a pluralityof treeblocks, wherein the treeblocks are rectangularly partitioned intoa hierarchy of progressively smaller sample blocks, said hierarchyrepresenting a hierarchical tree structure; determining an indication offurther partitioning the progressively smaller sample blocks into leafnodes, with each of the leaf nodes corresponding to a respectiveprediction block for a respective CU of a plurality of CUs; andgenerating a sample block for each CU of the plurality of CUs.
 39. Themethod according to claim 38, wherein each of the respective sampleblocks for each CU of the plurality of CUs may vary in size, furthercomprising: selectively executing said method if the respective sampleblock is 8×8.
 40. The method according to claim 38, wherein each of therespective sample blocks for each CU of the plurality of CUs may vary insize, further comprising: selectively executing said method if therespective sample block is 32×32 or 16×16.
 41. The method according toclaim 38, wherein each of the respective sample blocks for each CU ofthe plurality of CUs may vary in size, further comprising: selectivelyexecuting said method if the respective sample block is 64×64.
 42. Themethod according to claim 32, wherein for each sample position of thesample block of the CU, said applying operation is adaptively executedin instances to enhance visual quality or compression performance. 43.The method according to claim 32, for each sample position of the sampleblock of the CU, said applying operation is adaptively executed based onthe size of the CU.
 44. The method according to claim 32, furthercomprising signaling the partitioning line parameters, the partitioningline parameters comprising at least two parameters such that thenon-perpendicular partitioning line is designated by the at least twoparameters.
 45. The method according to claim 44, wherein thepartitioning line parameters indicate an angle and define a location ofthe non-perpendicular partitioning line.
 46. A computing device thatencodes video data, the computing device comprising: a storage mediumconfigured to store a sample block of a coding unit (CU) of a picture ofthe video data; and a processor in communication with the storagemedium, wherein the processor is configured to: receive the sample blockof the CU; geometrically partition the sample block of the CU into afirst area of samples and a second area of samples with respect to anon-perpendicular partitioning line using a geometric partitioning mode,and identifying the non-perpendicular partitioning line by one or morepartitioning line parameters; perform motion estimation with respect tothe first area to generate first prediction parameters associated with afirst prediction unit (PU) of the CU, wherein the first predictionparameters include a first motion vector; perform motion estimation withrespect to the second area to generate second prediction parametersassociated with a second prediction unit (PU) of the CU; identify atransition zone of samples from the first area and the second area basedon the non-perpendicular partitioning line and a size of the CU, whereinthe processor is configured such that, as part of identifying thetransition zone, the processor, for each respective sample of the firstPU: determines whether any sample in a respective 2-dimensional regionof samples for the respective sample of the first PU is in the secondPU, wherein the respective 2-dimensional region of samples for therespective sample of the first PU is centered on the respective sampleof the first PU and has a region size determined based on the size ofthe CU; and identifies whether the respective sample of the first PU isin the transition zone, wherein the respective sample of the first PU isidentified as being in the transition zone in response to determining asample in the respective 2-dimensional region of samples for therespective sample of the first PU is in the second PU and the respectivesample of the first PU is identified as not being in the transition zonein response to determining no sample in the respective 2-dimensionalregion of samples for the respective sample of the first PU is in thesecond PU; and for each sample position of the sample block of the CU,the processor is further configured to: determine a first pixel valuecorresponding to the first prediction parameters, determine a secondpixel value corresponding to the second prediction parameters, and applya weighted sum of the first prediction values and the second predictionvalues of samples to produce a prediction block for the CU.
 47. Thecomputing device according to claim 46, wherein the second predictionparameters include a second motion vector and the processor is furtherconfigured to: subtract the predicted sample block from the sample blockof the CU to generate residual data; and transmit the first predictionparameters, the second prediction parameters, the first motion vector,the second motion vector, the one or more partitioning line parameters,and the residual data as an encoded CU of the video data.
 48. Thecomputing device according to claim 46, wherein the second predictionparameters include second motion information, and the predicted samplecorresponds to a calculated weighted average of first motion compensatedprediction associated with the first PU and second motion compensatedprediction associated with the second PU.
 49. The computing deviceaccording to claim 46, wherein the applied weighted sum for each sampleposition of the prediction block for the CU to be predicted provides afilter-based smoothing operation to the samples in the transition zonecorresponding to one or more predetermined filters.
 50. The computingdevice according to claim 49, wherein each of the one or morepredetermined filters is a set of predetermined weights set forth in apredetermined table.
 51. The computing device according to claim 49,wherein each of the one or more predetermined filters is a set ofpredetermined weights set forth in a predetermined table such that eachof the plurality of weights increases in value with respect to distancefrom the non-perpendicular partitioning line.
 52. The computing deviceaccording to claim 49, wherein the processor is further configured to:determine an indication for rectangularly partitioning a frame of thevideo data into a plurality of treeblocks, wherein the treeblocks arerectangularly partitioned into a hierarchy of progressively smallersample blocks, said hierarchy representing a hierarchical treestructure; determine an indication of further partitioning theprogressively smaller sample blocks into leaf nodes, with each of theleaf nodes corresponding to a respective prediction block for arespective CU of the plurality of CUs; and generate a sample block foreach CU of the plurality of CUs.
 53. The computing device according toclaim 52, wherein each of the respective sample blocks for each CU ofthe plurality of CUs may vary in size, the processor further configuredto: selectively use the geometric partitioning mode if the respectivesample block is 8×8.
 54. The computing device according to claim 52,wherein each of the respective sample blocks for each CU of theplurality of CUs may vary in size, the processor further configured to:selectively use the geometric partitioning mode if the respective sampleblock is 32×32 or 16×16.
 55. The computing device according to claim 52,wherein each of the respective sample blocks for each CU of theplurality of CUs may vary in size, the processor further configured to:selectively use the geometric partitioning mode if the respective sampleblock is 64×64.
 56. The computing device according to claim 49, whereinfor each sample position of the sample block of the CU, the processor isfurther configured to adaptively execute the geometric partitioning modein instances to enhance visual quality or compression performance. 57.The computing device according to claim 49, for each sample position ofthe sample block of the CU, the processor is further configured toadaptively execute the geometric partitioning mode based on the size ofthe CU.
 58. The computing device according to claim 49, wherein theprocessor is configured to signal the one or more partitioning lineparameters, the partitioning line parameters comprising at least twoparameters such that the non-perpendicular partitioning line isdesignated by the at least two parameters.
 59. The computing deviceaccording to claim 58, wherein the partitioning line parameters indicatean angle and define a location of the non-perpendicular partitioningline.